TWI777583B - Water resources treatment method and apparatus thereof - Google Patents

Abstract

A method of treating water resources by an electronic device is provided. The method includes obtaining at least one oxidation-reduction potential (ORP) target value. A first ORP parameter generated by detecting a water body at first detection time is received from an ORP detecting apparatus. The first ORP parameter is compared with the at least one ORP target value to generate a first comparison result. A control parameter is generated based on the first comparison result, and an operation parameter of a controlled device for the water body is adjusted based on the control parameter.

Description

Water resource treatment method and device

The invention relates to a water resource treatment method and device.

With the increasing awareness of environmental protection, biological treatment procedures are usually used to remove organic water pollutants and biological nutrients such as nitrogen and phosphorus in the wastewater. Biological treatment procedures mainly include biological activated sludge procedures and biological nitrogen and phosphorus removal procedures. Among them, the effectiveness factors affecting the biological activated sludge process and the biological nitrogen and phosphorus removal process include the amount of microorganisms, the time of biochemical reaction, the activity of microorganisms, the supply of carbon sources and the number of electron acceptors. The monitoring parameters corresponding to the above factor responses in the program operation are the mixed liquor suspended solids (Mixed liquor suspended solids, MLSS), the sludge retention time (Sludge retention time, SRT), the hydraulic retention time (Hydraulic retention time, HRT), Activated sludge oxygen uptake rate (Oxygen uptake rate, OUR) or specific oxygen uptake rate (Specific oxygen uptake rate, SOUR), sludge bed settling velocity (Zone settling velocity, ZSV), carbon and nitrogen ratio required for nitrogen and phosphorus removal (C/N), carbon to phosphorus ratio (C/P), dissolved oxygen (DO). However, among the aforementioned factors, only MLSS and DO parameters can be obtained immediately, and other parameters can only be obtained after a long period of manual laboratory analysis, so they are no longer effective monitoring parameters, making it difficult for biological treatment procedures to target all factors are monitored and adjusted.

In addition, in the design of biological treatment programs, in order to comply with the protection of receiving water bodies and discharge water standards, they are usually designed in a steady-state inflow way (that is, in a way that the inflow water volume and water quality will not change at any time). . However, in the process of sewage treatment, it is usually a dynamic and very complex process. The amount of influent water and water quality are constantly changing with time. At the same time, the biological activated sludge process and biological nitrogen and phosphorus removal process are controlled by microbial metabolism, mechanical operation and The interaction and influence of environmental conditions and other factors will have an impact on the water quality treatment effect of the sewage treatment system and the stability of the system operation. Therefore, in the traditional automatic control system designed by the steady-state control method, the factors listed above are often reduced to human control rather than automatic control.

It is necessary to provide a water resource treatment method and system, aiming at solving the technical problem that the intelligence of the sewage treatment system cannot be realized in the prior art.

An electronic device for water treatment. The electronic device includes at least one processor and a storage device. The storage device is coupled to the at least one processor and stores a plurality of instructions that, when executed by the at least one processor, cause the at least one processor to obtain at least one ORP target value; the receiving ORP sensing device is in A first ORP parameter sensed by a water body to be measured at a first sensing time; comparing the first ORP parameter with the at least one ORP target value to generate a first comparison result; generating according to the first comparison result A control parameter; and an operation parameter of a controlled device for processing the water body to be measured is regulated according to the control parameter.

A method of water treatment for an electronic device. The method includes obtaining at least one ORP target value. A first ORP parameter sensed by the ORP sensing device for a water body to be measured at a first sensing moment is received. Combining the first ORP parameter with the at least one ORP target value The comparison is performed to produce the first comparison result. A control parameter is generated according to the first comparison result, and an operation parameter of a controlled device for processing the water body to be measured is regulated according to the control parameter.

1: Water resources monitoring and treatment system

10: Water to be tested

11, 411-413, 611-613, 711-713, 911-914: Sensing devices

12: The first control device

13: Second control device

14: Controlled device

221: Processor

222: Storage

2221: Operating System

2222: Parameter monitoring and water treatment module

223: Signal receiver

224: Signal Transmitter

S310-S350: Steps

4: Instant aeration control system

4411-4412, 941, 1941: Blowers

4421-4422, 642, 742, 942: Inverter

4431-4432: Electric valve

4511-4516: Pressure Sensing Device

452: Air Flow Meter

S510-S550, S561-S562, S571-S573, S580: Steps

6: Internal reflux control system of nitrification solution

601, 701, 1901: Anaerobic ponds

602, 702, 11021-11023, 1902: Hypoxic Pool

603, 703, 903, 11031-11033, 1903: Aerobic Pool

604, 704, 1904: Settlement ponds

643, 1943: Internal return pump

644, 744, 1944: Sludge return pump

7: Sludge return control system

S810-S850, S861-S862, S871-S873, S880: Steps

9: Intermittent aeration control system

945: Flow Booster

S1010-S1060, S1071-S1072, S1081-S1082: Steps

110: Segmented inflow control system

11111-11113, 11121-11123, 1611-1612, 1911-1913: Sensing devices

11461-11463: Gate valve

S1210-S1250, S1310-S1340, S1510-S1540, S1710-S1750: Steps

160: Sludge Settling Monitoring System

1605: Test Pool

1653: Sampling Pump

1654: Return valve

1810: During the retardation of subsidence

1820: Conversion during settlement

1830: During compaction settling

1960: Bioactivity indicator monitoring device

FIG. 1 is a schematic diagram of a water resource monitoring and treatment system of an example embodiment of the present invention.

FIG. 2 is a schematic diagram of a first control device having a parameter monitoring and water resource treatment module in an exemplary embodiment of the present invention.

FIG. 3 is a flowchart of a parameter monitoring and water resource treatment method according to an exemplary embodiment of the present invention.

4 is a schematic diagram of an instant aeration control system in a water resource monitoring and treatment system according to an example embodiment of the present invention.

FIG. 5 is a flow chart of the instant aeration control process in the parameter monitoring and water resource treatment method of the exemplary implementation method of the present invention.

FIG. 6 is a schematic diagram of a nitrification liquid internal reflux control system in a water resource monitoring and treatment system according to an exemplary embodiment of the present invention.

7 is a schematic diagram of a sludge return control system in a water resource monitoring and treatment system according to an exemplary embodiment of the present invention.

FIG. 8 is a flow chart of a backflow control process in a parameter monitoring and water resource treatment method of an exemplary implementation method of the present invention.

9 is a schematic diagram of an intermittent aeration control system in a water resource monitoring and treatment system of an exemplary embodiment of the present invention.

10 is a flow chart of the intermittent aeration control process in the parameter monitoring and water resources treatment method of the exemplary implementation method of the present invention.

11 is a schematic diagram of a segmented inflow control system in a water resource monitoring and treatment system according to an example embodiment of the present invention.

FIG. 12 is a flow chart of the segmented inflow control process in the parameter monitoring and water resources processing method of the exemplary implementation method of the present invention.

13 is a flow chart of the biological nitrification/denitrification program control process in the parameter monitoring and water resources treatment method of the exemplary implementation method of the present invention.

Figure 14 shows the changes of ORP variables and pH variables under the biological nitrification/denitrification reaction of the water body to be tested during the water resource treatment process of the water body to be tested.

FIG. 15 is a flow chart of the biological activity index monitoring process in the parameter monitoring and water resource treatment method of the exemplary implementation method of the present invention.

16 is a schematic diagram of a sludge settling monitoring system in a water resource monitoring and treatment system according to an exemplary embodiment of the present invention.

FIG. 17 is a flow chart of the process of monitoring the sludge settleability in the parameter monitoring and water resources treatment method of the exemplary implementation method of the present invention.

Fig. 18 shows the sedimentation changes of the MLSS at different depths of the water to be measured under the process of monitoring the sludge settleability in the process of water resource treatment for the water to be measured.

19 is a schematic diagram of a water resource monitoring and treatment system of an example embodiment of the present invention.

The following description contains specific information related to exemplary embodiments of the present invention. The drawings of this disclosure and the detailed description that accompany it are merely exemplary embodiments. However, the present invention is not limited to these exemplary embodiments. Other variations and embodiments of the invention will occur to those skilled in the art. Identical or corresponding elements in the figures may be identical or corresponding unless otherwise indicated indicated by the reference number. Furthermore, the drawings and illustrations in this disclosure are generally not to scale and are not intended to correspond to actual relative dimensions.

For purposes of consistency and ease of understanding, like features are designated by reference numerals in the exemplary drawings (although in some instances they are not so designated). However, the features in different embodiments may differ in other respects and therefore should not be narrowly limited to the features shown in the drawings.

For terms such as "at least one example embodiment," "an example embodiment," "example embodiments," "different example embodiments," "some example embodiments," "example embodiments of this invention," and the like, It may be indicated that embodiments of the invention so described may include a particular feature, structure, or characteristic, but not every possible embodiment of the invention necessarily includes a particular feature, structure, or characteristic. Furthermore, repeated use of the phrases "in at least one example embodiment," "in an example embodiment," "the example embodiment" does not necessarily refer to the same embodiment, although they may. Furthermore, the use of phrases such as "embodiments" in connection with "the present invention" does not imply that all embodiments of the present invention necessarily include a particular feature, structure or characteristic, and should be understood as "at least some embodiments of the present invention" ” includes the particular feature, structure or characteristic described. The term "coupled" is defined as connected, whether directly or indirectly through intervening elements, and is not necessarily limited to physical connections. When the term "including" is used, it means "including but not limited to," which expressly indicates the open inclusion or relationship of the stated combinations, groups, series, and equivalents.

Furthermore, for purposes of explanation and not limitation, specific details are set forth such as functional entities, techniques, protocols, standards, etc. to provide an understanding of the described technology. In other instances, detailed descriptions of well-known methods, techniques, systems, architectures, etc. are omitted so as not to obscure the description with unnecessary detail.

The terms "first", "second" and "third" in the description of the present invention and the above-mentioned drawings are used to distinguish different items, rather than to describe a specific order. Furthermore, the term "Includes" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or modules is not limited to the listed steps or modules, but may optionally also include unlisted steps or modules, or alternatively It also includes other steps or modules inherent to these processes, methods, products or devices.

Those skilled in the art will immediately recognize that any computing function or algorithm described in this disclosure may be implemented in hardware, software, or a combination of software and hardware. The modules corresponding to the described functions may be software, hardware, firmware or any combination thereof. A software implementation may include computer-executable instructions stored on a computer-readable medium, such as a memory or other type of storage. For example, one or more microprocessors or general-purpose computers with communications processing capabilities may use corresponding executable instructions to program and execute the network functions or algorithms described. The processor, microprocessor or general-purpose computer can be formed by ASIC (Applications Specific Integrated Circuitry), programmable logic array and/or using one or more digital signal processors (DSP). Although several of the exemplary embodiments described in this specification are directed towards software installed and executed on computer hardware, alternative exemplary embodiments in which embodiments are implemented in firmware or hardware or a combination of hardware and software are also contemplated herein. within the scope of the invention.

Computer readable media include but are not limited to random access memory RAM (Random Access Memory), read only memory ROM (Read Only Memory), erasable programmable read only memory EPROM (Erasable Programmable Read-Only Memory), Electrically Erasable Programmable Read-Only Memory EEPROM (Electrically Erasable Programmable Read-Only Memory), Flash Memory, Compact Disc Read-Only Memory CD ROM (Compact Disc Read-Only Memory), Magnetic Box, Tape, Disk Memory body or any other equivalent medium capable of storing computer readable instructions.

The coupling between the devices of the present invention can adopt customized protocols or follow existing standards or de facto standards, including but not limited to Ethernet, IEEE 802.11 or IEEE 802.15 series, wireless USB or telecommunication standards (including but not limited to GSM (Global System for Mobile Communications, Global System for Mobile Communications), CDMA2000 (Code Division Multiple Access, Code Division Multiple Access technology), TD-SCDMA (Time Division-Synchronization Code Division Multiple Access, Time Division Synchronization Code Division Multiple Access technology), WiMAX (World Interoperability for Microwave Access, World Interoperability for Microwave Access), 3GPP-LTE (Long Term Evolution, long term evolution technology) or TD-LTE (Time Division Long Term Evolution, time division long term evolution technology)). In addition, the apparatuses of the present invention may each include any device configured to transmit and/or store data to and receive data from a computer-readable medium. Furthermore, each device of the present invention may include a computer system interface that may enable data to be stored on or received from the storage device. For example, each device of the present invention may include a chip set supporting Peripheral Component Interconnect (PCI) and Peripheral Component Interconnect Express (PCIe) bus protocols, a dedicated bus protocol, a Universal Serial Bus (Universal) Serial Bus, USB) protocol, I2C, or any other logical and physical structure that can be used to interconnect peer devices.

The specific embodiments of the method and system for monitoring and treating water resources of the present invention will be described below with reference to the accompanying drawings.

Please refer to FIG. 1 , which is a schematic diagram of a water resource monitoring and treatment system 1 according to an exemplary embodiment of the present invention. The water resource monitoring and processing system 1 includes a plurality of sensing devices 11 , a first control device 12 , a second control device 13 and a plurality of controlled devices 14 . In this exemplary embodiment, the water resource monitoring and processing system 1 measures a water body 10 to be measured through the plurality of sensing devices 11 to obtain a plurality of sensing parameters. In this example embodiment, the first control device 12 Using the plurality of acquired sensing parameters, a plurality of first control parameters are generated through a first operation mode, and the plurality of controlled devices 14 are regulated according to the plurality of first control parameters. In this exemplary embodiment, the second control device 13 also uses the acquired sensing parameters to generate a plurality of second control parameters through a second operation mode, and the plurality of second control parameters can be used to replace The plurality of first control parameters are used to regulate the plurality of controlled devices 14 . In this example embodiment, the plurality of controlled devices 14 perform water resource treatment on the water body 10 to be tested. In this exemplary embodiment, the water treatment can be water treatment including biological activated sludge procedures and biological denitrification and phosphorus removal procedures.

In at least one example embodiment, the plurality of sensing devices 11 can each sense the water body 10 to be measured, so as to obtain one of the plurality of sensing parameters. In at least one example embodiment, the plurality of sensing devices 11 may include a mixed liquor suspended solids (MLSS) sensing device, a dissolved oxygen (Dissolved Oxygen, DO) sensing device, a pH value ( at least one of a pH) sensing device and an oxidation-reduction potential (Oxidation-Reduction Potential, ORP) sensing device. In this exemplary embodiment, the MLSS sensing device is used to measure the water body 10 to be measured to obtain an MLSS parameter. In this exemplary embodiment, the DO sensing device is used to measure the water body 10 to be measured to obtain a DO parameter. In this exemplary embodiment, the pH sensing device is used to measure the water body 10 to be measured to obtain a pH parameter. In this exemplary embodiment, the ORP sensing device is used to measure the water body 10 to be measured to obtain an ORP parameter. In the example embodiment, the plurality of sensed parameters may include at least one of the MLSS parameter, the DO parameter, the pH parameter, and the ORP parameter.

In at least one example embodiment, the first control device 12 uses the acquired sensing parameters to generate the first control parameters. In this example embodiment, the plurality of first control parameters are a plurality of numbers calculated by the first control device 12 for a plurality of control variables value. In this example embodiment, the plurality of control variables may include sludge return flow, biological nitrification/denitrification reaction endpoint, aeration amount, staged inflow flow, nitrification liquid internal return flow, and DO value.

In at least one example embodiment, the first control device 12 can be coupled with at least one of the plurality of sensing devices 11 to obtain at least one of the plurality of sensing parameters, and obtain at least one of the plurality of sensing parameters through the obtained at least one A sensing parameter, and one of the plurality of first control parameters is obtained by using the first operation mode. For example, the first control device 12 can be coupled with the pH sensing device and the ORP sensing device to obtain the pH parameter and the ORP parameter of the water body 10 to be measured, and obtain through the first operation mode The segment inflow.

In at least one example embodiment, the first control device 12 can be directly connected to the plurality of sensing devices 11 through a wire. In at least one example embodiment, if the plurality of sensing devices 11 have the function of wireless transmission, the first control device 12 can be connected with the plurality of sensing devices 11 in a wireless manner. In at least one example embodiment, the first control device 12 can be directly connected to a part of the plurality of sensing devices 11 through a wired way, and can be connected to the other plurality of sensing devices 11 through a wireless way.

In at least one example embodiment, the first control device 12 can be an electronic device. The electronic device can be a single computing device, an edge computing system, or a combination of a single computing device and an edge computing system. In this example embodiment, the single computing device can obtain all the sensing parameters, and use the first computing mode to obtain all the first control parameters. In the exemplary embodiment, if the single computing device only regulates a part of the plurality of control variables and does not regulate the other plurality of control variables, the single computing device may only obtain the plurality of control variables corresponding to the part of the control variables A part of the parameters is sensed, and the first operation mode is used to obtain part of the plurality of first control parameters.

In at least one example embodiment, the edge computing system may include a plurality of edge computing units. In this example embodiment, the plurality of edge operation units may each be used to operate the first at least one of a control parameter. Therefore, the plurality of edge operation units can each obtain a part of the plurality of sensing parameters corresponding to the at least one first control parameter calculated by themselves, and use the first operation mode to obtain the first control calculated by themselves. parameter. For example, if a specific edge computing unit can be used to calculate the segment inflow and the sludge return flow, the specific edge computing unit can be used with the pH sensing device, the ORP sensing device and the MLSS sensing device. The device is coupled to obtain the pH parameter, the ORP parameter and the MLSS parameter of the water body 10 to be tested, and obtain the segment inflow and the sludge return volume through the first operation mode, and use the segment At least one of the plurality of controlled devices 14 corresponding to the regulation of the inflow volume and the sludge return volume.

In at least one example embodiment, the first control device 12 may be the combination of the computing device and the edge computing system. The single computing device can obtain all the plurality of sensing parameters and provide the edge computing system for subsequent computing.

In at least one example embodiment, the first operation mode used by the first control device 12 may be a fuzzy logic control mode. In this example embodiment, when the first control device 12 uses the fuzzy logic control mode to calculate the different first control parameters, the first control device 12 uses different first control parameters in the fuzzy logic control mode an operation process. Therefore, each of the plurality of first control parameters corresponds to one of the plurality of first operation processes.

In at least one example embodiment, the first control device 12 provides a visual interface. The visual interface is used to provide a human-computer interaction interface to the user, and the user can obtain the visual interface by connecting to the first control device 12 through other electronic devices such as a mobile phone or a computer.

In at least one example embodiment, the second control device 13 can be wirelessly connected with the first control device 12 to obtain the required sensing parameters through the first control device 12 . In at least one example embodiment, if the plurality of sensing devices 11 have the function of wireless transmission, the second control device 13 can communicate with the plurality of sensing devices 11 in a wireless manner A connection is made to directly obtain the plurality of sensing parameters. In at least one example embodiment, the second control device 13 can be wirelessly connected with a part of the plurality of sensing devices 11 to directly obtain a part of the plurality of sensing parameters, and obtain a part of the plurality of sensing parameters from the first The control device 12 obtains the other plurality of sensing parameters. In at least one example embodiment, the first control device 12 and the second control device 13 can perform data transmission according to a preset protocol. In this example implementation, the preset protocol includes, but is not limited to, any one of the following: HTTP protocol (Hyper Text Transfer Protocol, hypertext transfer protocol), HTTPS protocol (Hyper Text Transfer Protocol over Secure Socket Layer, with security as the goal) HTTP protocol), etc.

In at least one example embodiment, the second control device 13 can be a cloud computing system. In this example embodiment, the cloud computing system can obtain all the plurality of sensing parameters, and use the second computing mode to obtain all the plurality of second control parameters. In this exemplary embodiment, if the second control device 13 only regulates a part of the plurality of control variables and does not regulate the other plurality of control variables, the second control device 13 can only obtain the corresponding part of the control variables part of the plurality of sensing parameters, and using the second operation mode to obtain part of the plurality of second control parameters. In this exemplary embodiment, the plurality of second control parameters are a plurality of values calculated by the second control device 13 for the plurality of control variables.

In at least one example embodiment, the cloud computing system may include a plurality of cloud computing units. In this example embodiment, each of the plurality of cloud computing units may be used for computing at least one of the second control parameters. Therefore, the plurality of cloud computing units can each obtain a part of the plurality of sensing parameters corresponding to the at least one second control parameter calculated by themselves, and use the second operation mode to obtain the second control calculated by themselves. parameter.

In at least one example embodiment, the second operation mode used by the second control device 13 can be modeled and predicted through a mode of artificial intelligence. In this example embodiment, the second operation mode can be implemented through an Artificial Neural Network (ANN) Architecture and other artificial intelligence architecture modeling. In the exemplary embodiment, the second control device 13 can establish a control decision model and an effect evaluation model through the ANN architecture.

In at least one example embodiment, the artificial intelligence modeling architecture may include an ANN, a single-layer neural network (Single-Layer Neural Network), a feedforward neural network (FNN), a feedback neural network (Feedback) Neural Network), Self-organizing Neural Network, Structural Self-adaptive Neural Network, Generative Adversarial Neural Network and Stochastic Neural Network Network). In this example embodiment, the FNN may include a single-layer FNN, a multi-layer FNN, and a linear neural network. In this example embodiment, the single-layer FNN may comprise a single-layer perceptron. In this example embodiment, the multi-layer FNN may include a Radial Basis Function (RBF) neural network, a Back Propagation (BP) neural network, a fully-connected neural network (Fully-Connected Neural Network) Network) and Convolutional Neural Network (CNN). In this example embodiment, the RBF neural network may include a General Regression Neural Network (GRNN) and a Probabilistic Neural Network (PNN). In this example embodiment, the linear neural network may comprise a Madline neural network. In this example embodiment, the feedback neural network may include Recurrent Neural Network (RNN), Brain-State-in-a-Box (BSB), and Hopfield Neural Network (Hopfield Neural Network). In this example embodiment, the RNN may comprise an Elman neural network. In this example embodiment, the Hopfield neural network may include a discrete Hopfield neural network as well as a continuous Hopfield neural network. In this example embodiment, the self-organizing neural network may include a Competitive Neural Network (Competitive Neural Network), an Adaptive Resonance Theory (ART) neural network, and a Self-Organizing Map (SOM) ) neural network. In this example In an embodiment, the structure adaptive neural network may include a Cascade-Correlation Network. In this example embodiment, the stochastic neural network may comprise a Boltzmann Machine.

In at least one example embodiment, the second control device 13 can use the acquired sensing parameters to predict the control variables that should be used by the control variables through the control decision model established by the ANN architecture. a plurality of second control parameters. In this example embodiment, one of the plurality of first control parameters and one of the plurality of second control parameters may collectively correspond to one of the plurality of control variables, so the plurality of second control parameters are available To replace the plurality of first control parameters, the plurality of controlled devices 14 are regulated.

In at least one example embodiment, the second control device 13 can use the predicted second control parameters to predict the water body 10 to be tested through the performance evaluation model established by the ANN framework. A water quality prediction parameter adjusted by a plurality of second control parameters. In this exemplary embodiment, the second control device 13 can determine whether the plurality of second controlled parameters need to be re-adjusted through the water quality prediction parameter. For example, if the predicted water quality parameter is not within a parameter range, it means that an actual water quality parameter of the water body 10 to be measured may exceed the predetermined time after the controlled device 14 is adjusted by the plurality of second controlled parameters for a predetermined period of time. Therefore, the second control device 13 can obtain the new plurality of second controlled parameters through the second operation mode again, so as to prevent the actual water quality parameter of the water body 10 to be measured from exceeding the predetermined time after the predetermined time. The parameter range. In addition, if the water quality prediction parameter is included in the parameter range, it means that the actual water quality parameter of the water body 10 to be measured is still maintained at the predetermined time after the controlled device 14 is adjusted by the plurality of second controlled parameters for the predetermined time. within this parameter range.

In at least one example embodiment, the plurality of controlled devices 14 can each provide different water resources treatment for the water body 10 to be tested. In at least one example embodiment, the plurality of controlled The device 14 may include various water treatment devices such as frequency converters, blowers, electric valves, internal return pumps, sludge return pumps, flow propellers, and gate valves for inflow water.

In at least one example embodiment, the plurality of controlled devices 14 operate according to respective operating parameters. In the example embodiment, the plurality of operating parameters are each calculated based on at least one of the plurality of first control parameters. In the example embodiment, the plurality of operating parameters are each calculated based on at least one of the plurality of second control parameters. In the example embodiment, the plurality of operating parameters of the plurality of controlled devices 14 may include the operating frequency of the frequency converter, the operating flow rate of the blower, the operating flow rate of the internal return pump, the operation of the sludge return pump flow rate, the operating frequency of the flow propeller, and the operating flow rate and orifice opening of the gate valve. In the exemplary embodiment, when the frequency converter is connected to the blower, the operation frequency of the blower can be regulated by regulating the frequency converter according to the plurality of first control parameters or the plurality of second control parameters. In the exemplary embodiment, when the frequency converter is connected to the internal return pump, the operation of the internal return pump can be regulated by regulating the frequency converter according to the plurality of first control parameters or the plurality of second control parameters frequency. In this example embodiment, when the frequency converter is connected to the sludge return pump, the sludge return pump can be regulated by regulating the frequency converter according to the plurality of first control parameters or the plurality of second control parameters operating frequency.

In at least one example embodiment, the plurality of controlled devices 14 can be coupled to the first control device 12 to receive the plurality of operating parameters generated by the first control device 12 according to the plurality of first control parameters to operate. In at least one example embodiment, the plurality of controlled devices 14 may be coupled with the first control device 12 to receive the plurality of first control parameters of the first control device 12 and further be controlled by the plurality of received The control device 14 generates the plurality of operation parameters by itself to operate. In at least one example embodiment, the coupling between the plurality of controlled devices 14 and the first control device 12 can be performed in a wired or wireless manner.

In at least one example embodiment, the plurality of controlled devices 14 can be coupled with the second control device 13 to receive the plurality of operating parameters generated by the second control device 13 according to the plurality of second control parameters to operate. In at least one example embodiment, the plurality of controlled devices 14 may be coupled with the second control device 13 to receive the plurality of second control parameters of the second control device 13, and further be controlled by the plurality of received The control device 14 generates the plurality of operating parameters by itself to operate. In at least one example embodiment, the coupling between the plurality of controlled devices 14 and the second control device 13 is performed wirelessly.

In at least one example embodiment, the plurality of controlled devices 14 may be coupled to the second control device 13 via the first control device 12 . In an exemplary embodiment, the second control device 13 generates the plurality of operating parameters according to the plurality of second control parameters, provides the plurality of operating parameters to the first control device 12, and then uses the first control device 12 to generate the plurality of operating parameters. The control device 12 provides the plurality of controlled devices 14 with corresponding operating parameters. In another exemplary embodiment, the second control device 13 provides the plurality of second control parameters to the first control device 12, and the first control device 12 generates the plurality of second control parameters according to the plurality of second control parameters operating parameters, and then provide the operating parameters corresponding to the plurality of controlled devices 14 . In yet another exemplary embodiment, the second control device 13 provides the plurality of second control parameters to the first control device 12 , and the first control device 12 provides the required control parameters corresponding to the plurality of controlled devices 14 At least one of the plurality of second control parameters is then generated by the plurality of controlled devices 14 according to at least one of the obtained plurality of second control parameters.

Please refer to FIG. 2 . FIG. 2 shows a schematic diagram of the first control device 12 having a parameter monitoring and water resource processing module according to an exemplary embodiment of the present invention. The first control device 12 has at least one processor 221 , at least one storage 222 , at least one signal receiver 223 and at least one signal transmitter 224 . In this example embodiment, the at least one signal receiver 223 may be coupled with the plurality of sensing devices 11 to receive the plurality of sensing parameters. In this example In an embodiment, the at least one signal transmitter 224 can be coupled with the second control device 13 to transmit the plurality of sensing parameters. In this example embodiment, the at least one signal receiver 223 may be coupled to the second control device 13 to receive the plurality of second control parameters or the plurality of operating parameters. In this example embodiment, the at least one signal transmitter 224 may be coupled with the plurality of controlled devices 14 to transmit the plurality of first control parameters, the plurality of second control parameters or the plurality of operating parameters.

In this example embodiment, the at least one processor 221 may invoke the program code stored in the at least one memory 222 to perform related functions. For example, the parameter monitoring and water resource processing module 2222 in FIG. 2 is a program code stored in the at least one memory 222 and executed by the at least one processor 221 .

In the example embodiment, the at least one storage 102 may be a storage device for storing code. The at least one storage 102 may include the operating system 2221 of the first control device 12 and the parameter monitoring and water resource processing module 2222 . In this example embodiment, the operating system 2221 is a program that manages and controls the hardware and software resources of the first control device 12 and supports the parameter monitoring and water treatment module 2222 and other software and/or programs. run.

FIG. 3 shows a flow chart of the parameter monitoring and water resource processing method of the exemplary specific implementation method of the present invention. Because of the variety of ways in which the exemplary method can be performed, the method is provided by way of example only. The methods described below may be performed using, for example, the systems and apparatus shown in FIGS. 1 and 2, and exemplary methods are explained with reference to various elements of these figures. Each block shown in FIG. 3 represents one or more routines, methods, or subroutines performed in the exemplary method. Furthermore, the order of the blocks is exemplary only and may vary. Additional blocks may be added or fewer blocks may be used without departing from the invention. The parameters shown in FIG. 3 are taken as an example of ORP parameters, so FIG. 3 can also be regarded as a flowchart of the ORP parameter monitoring and water resource processing method of the exemplary embodiment of the present invention.

At block S310, the first control device 12 obtains at least one ORP target value.

In at least one example embodiment, the first control device 12 obtains the at least one ORP target value in a predetermined manner. In the exemplary embodiment, the preset mode can directly read the at least one ORP target value from the internal storage 222 through the first control device 12 . In another exemplary embodiment, the predetermined manner may generate the ORP target value through the first control device 12 through an ORP target value determination method.

In at least one example embodiment, the at least one ORP target value may include only a single ORP target value. In at least one example embodiment, the at least one ORP target value may include more than two ORP target values. In this example embodiment, when the number of the at least one ORP target value is 1, the value of the ORP variable may be divided into a first ORP value region greater than or equal to the ORP target value and a first ORP value region less than the ORP target value Two ORP numerical fields. In this example embodiment, when the number of the at least one ORP target value is 2, the value of the ORP variable can be divided into a first value region greater than or equal to a first ORP target value, less than the first ORP target value A second value region with a value greater than a second ORP target value and a third value region less than or equal to the second ORP target value. In the example embodiment, the first ORP target value is greater than the second ORP target value.

At block S320, the first control device 12 receives a first ORP parameter sensed from an ORP sensing device at a first sensing moment.

In at least one example embodiment, the plurality of sensing devices 11 comprise the ORP sensing device. The ORP sensing device is placed in the water body 10 to be tested to continuously sense the ORP parameters of the water body 10 to be tested. In this exemplary embodiment, the ORP sensing device can periodically sense the ORP parameter of the water body 10 to be tested, and can also continuously sense the ORP parameter of the water body 10 to be tested. In this exemplary embodiment, if the ORP sensing device periodically senses the ORP parameter of the water body 10 to be tested, the ORP sensing device has a sensing period. In this example embodiment, if the ORP The sensing device regularly uploads the ORP parameters of the water body 10 to be measured to the first control device 12, and the first control device 12 has a receiving cycle. In this exemplary embodiment, when the first control device 12 performs subsequent processing with the value of the ORP variable, it may have a processing cycle. In at least one example embodiment, the first control device 12 may generate a control period Δt of the ORP variable according to at least one of the sensing period, the receiving period and the processing period.

At block S330, the first control device 12 compares the first ORP parameter with the at least one ORP target value to generate a first comparison result.

In at least one example embodiment, when the number of the at least one ORP target value is 1, the first comparison result represents that the first ORP parameter is located in one of the first value field and the second value field . In this example embodiment, the first comparison result is whether the first ORP parameter is greater than or equal to the at least one ORP target value. In at least one example embodiment, when the number of the at least one ORP target value is 2, the first comparison result represents that the first ORP parameter is located in the first value field, the second value field and the third value field one of the numeric fields. In this example embodiment, the first comparison result is that the first ORP parameter is greater than or equal to the first ORP target value, the first ORP parameter is less than the first ORP target value and greater than or equal to the second ORP target value Or the first ORP parameter is less than the second ORP target value.

At block S340, the first control device 12 generates a control parameter according to the first comparison result.

In at least one example embodiment, the first control device 12 can operate a plurality of control variables, and different control variables have different values at different time points. In an exemplary embodiment, the first control device 12 can calculate an initial parameter of the plurality of controlled devices 14 at the first sensing time according to the operating conditions of the plurality of controlled devices 14, and according to the The initial parameters and the first comparison result determine the control parameters.

In an exemplary embodiment, the first control device 12 may directly generate the control parameter according to the first comparison result. In another exemplary embodiment, the first control device 12 may additionally receive a second ORP parameter sensed by the ORP sensing device on the water body to be measured at a second sensing time. In this example embodiment, the second sensing time is earlier or later than the first sensing time, and the time difference between the second sensing time and the first sensing time may be equal to n×Δt. In this example embodiment, n is a positive integer. In this example embodiment, the first control device 12 may compare the first ORP parameter with the second ORP parameter to generate a second comparison result, and according to at least one of the first comparison result and the second comparison result A, to generate the control parameter.

In an exemplary embodiment, the first control device 12 may first determine how to compare the first ORP parameter and the second ORP parameter according to the first comparison result. In this example embodiment, the second sensing time is later than the first sensing time. In the example embodiment, when the first ORP parameter is located in the first ORP value region greater than or equal to the ORP target value, the first control device 12 can confirm whether the second ORP parameter is greater than or equal to the first ORP value ORP parameters. If the second ORP parameter is greater than or equal to the first ORP parameter, the first control device 12 may generate a control coefficient Kp according to the at least one ORP target value, so as to generate the control according to the initial parameter and the control coefficient Kp parameter. In this example embodiment, the control parameter may be equal to the product of the initial parameter and the control coefficient Kp. If the second ORP parameter is smaller than the first ORP parameter, the first control device 12 may set the control coefficient Kp equal to 1, and the control parameter may be equal to the initial parameter. In this example embodiment, when the first ORP parameter is located in the second ORP value region smaller than the ORP target value, the first control device 12 may confirm whether the second ORP parameter is smaller than the first ORP parameter. If the second ORP parameter is smaller than the first ORP parameter, the first control device 12 may generate the control coefficient Kp according to the at least one ORP target value, so as to generate the control parameter according to the initial parameter and the control coefficient Kp. If the second ORP When the parameter is greater than or equal to the first ORP parameter, the first control device 12 can set the control coefficient Kp equal to 1, and the control parameter can be equal to the initial parameter.

In an exemplary embodiment, the first control device 12 may first determine whether to obtain the second ORP parameter according to the first comparison result. In an exemplary embodiment, when the first ORP parameter is located in the first ORP numerical region greater than or equal to the first ORP target value or in the third ORP numerical region less than or equal to the second ORP target value, The first control device 12 may generate the control parameter directly according to the first comparison result without obtaining the second ORP parameter. In this example embodiment, the first control device 12 can directly generate the control parameter according to a parameter extreme value of the controlled device 14 . In an example embodiment, when the first ORP parameter is located in the second ORP value region that is less than the first ORP target value and greater than the second ORP target value, the first control device 12 may receive the ORP sense from the ORP sensor. measuring the second ORP parameter, and comparing the first ORP parameter with the second ORP parameter to obtain the second comparison result. In this example embodiment, the second sensing moment is earlier than the first sensing moment. If the second ORP parameter is greater than the first ORP parameter, the first control device 12 may generate the control coefficient Kp according to the first ORP target value, so as to generate the control parameter according to the initial parameter and the control coefficient Kp. If the second ORP parameter is less than or equal to the first ORP parameter, the first control device 12 may set the control coefficient Kp equal to 1, and the control parameter may be equal to the initial parameter.

At block S350, the first control device 12 regulates an operating parameter of a controlled device 14 according to the control parameter.

In at least one example embodiment, different controlled devices 14 will have different operating parameters. In at least one example embodiment, different control parameters may regulate different operating parameters.

In an exemplary embodiment, if the control parameter is the aeration amount, the operating parameter may include the operating frequency of the frequency converter, the operating flow of the blower and the flow rate of the flow propeller. at least one of the operating frequencies. In an example embodiment, if the control parameter is the internal return flow, the operating parameter may include at least one of the operating frequency of the frequency converter and the operating flow of the internal return pump. In an exemplary embodiment, if the control parameter is the sludge return flow rate, the operation parameter may include at least one of the operation frequency of the frequency converter and the operation flow rate of the sludge return pump. In an example embodiment, if the control parameter is the segment inflow volume, the operating parameter may include at least one of the operating flow and the orifice opening of the gate valve.

In at least one example embodiment, the operating parameter may be directly calculated by the first control device 12 and provided to the plurality of controlled devices 14 . In another embodiment, the first control device 12 can provide the control parameter to the plurality of controlled devices 14, and then each of the plurality of controlled devices 14 generates the respective operation parameter according to the control parameter, and According to the operating parameters, water resource treatment is performed on the water body to be measured.

Please refer to FIG. 4 . FIG. 4 is a schematic diagram of the instant aeration control system 4 of the water resource monitoring and treatment system 1 according to an exemplary embodiment of the present invention. The real-time aeration control system 4 includes a first control device 12, a plurality of sensing devices 411, 412 and 413, a plurality of blowers 4411 and 4412, a plurality of frequency converters 4421 and 4422, a plurality of electric valves 4431 and 4432, a plurality of pressure sensing devices 4511-4516 and an air flow meter 452. In this exemplary embodiment, the real-time aeration control system 4 measures the water body 10 to be measured through the plurality of sensing devices 411 , 412 and 413 to obtain a plurality of sensing parameters. In the exemplary embodiment, the first control device 12 uses the acquired sensing parameters to generate the first control parameters through the first operation mode, and generates the first control parameters according to the first control parameters. The plurality of blowers 4411 and 4412, the plurality of frequency converters 4421 and 4422, and the plurality of electric valves 4431 and 4432 are regulated and controlled.

In at least one example embodiment, the real-time aeration control system 4 further has the second control device 13 shown in FIG. 1 , and the second control device 13 also utilizes the acquired sensing parameters. A plurality of second control parameters are generated through the second operation mode, and the plurality of second control parameters can be used to replace the plurality of first control parameters to regulate the plurality of blowers 4411 and 4412, the plurality of frequency converters 4421 and 4422 and the plurality of electric valves 4431 and 4432.

In at least one example embodiment, the plurality of sensing devices 411 , 412 and 413 may be the ORP sensing device, the DO sensing device and the pH sensing device, respectively. In this exemplary embodiment, the blowers 4411 and 4412 , the frequency converters 4421 and 4422 and the electric valves 4431 and 4432 are all one of the controlled devices described in FIG. 1 .

In at least one example embodiment, the plurality of pressure sensing devices 4511-4516 and the air flow meter 452 are a plurality of detection devices, and the detection results of the plurality of detection devices can be used as the real-time aeration control system 4 Monitoring of additional information in . In at least one example embodiment, the plurality of detection results may not affect the confirmation result of the control parameter.

FIG. 5 shows a flow chart of the instant aeration control process in the parameter monitoring and water resources treatment method of the exemplary specific implementation method of the present invention. Because of the variety of ways in which the exemplary method can be performed, the method is provided by way of example only. The methods described below may be performed using, for example, the systems and apparatus shown in FIGS. 1, 2, and 4, and exemplary methods are explained with reference to various elements of these figures. Each block shown in FIG. 5 represents one or more routines, methods, or subroutines performed in the exemplary method. Furthermore, the order of the blocks is exemplary only and may vary. Additional blocks may be added or fewer blocks may be used without departing from the invention. In addition, FIG. 5 is a flowchart of a specific implementation method of the parameter monitoring and water resource treatment method shown in FIG. 3 .

At block S510, the first control device 12 obtains an ORP target value and a DO target range.

In at least one example embodiment, the first control device 12 obtains the ORP target value in a predetermined manner. In the exemplary embodiment, the preset mode can directly read the ORP target value from the internal storage 222 through the first control device 12 . Implemented in another example In the default mode, the ORP target value can be generated by the first control device 12 through an ORP target value determination method.

In at least one example embodiment, the first control device 12 obtains the DO target range through the preset method. In the exemplary embodiment, the preset mode can directly read the DO target range from the internal storage 222 through the first control device 12 . In the example embodiment, the DO target range includes a lower DO threshold and an upper DO threshold.

In at least one example embodiment, the first control device 12 obtains the numbers, operation relational expressions, minimum frequency, maximum frequency and initial frequency of the plurality of blowers 4411 and 4412 .

At block S520, the first control device 12 receives a first ORP parameter sensed by the ORP sensing device 411 at a first sensing moment and the DO sensing device 412 sensed at the first sensing moment to a DO parameter.

In at least one example embodiment, the ORP sensing device 411 and the DO sensing device 412 can periodically sense the ORP parameter and the DO parameter of the water body 10 to be measured, and can also continuously sense the water body to be measured 10 of the ORP parameter and the DO parameter. In this exemplary embodiment, the first control device 12 may have a control period Δt in the time interval between each time obtaining the values of the ORP variable and the DO variable.

At block S530, the first control device 12 confirms whether the DO parameter is within the DO target range. When the DO parameter is not within the DO target range, the method will proceed to block S540. When the DO parameter is within the DO target range, the method will proceed to block S550.

In at least one example embodiment, when the DO parameter is not within the DO target range, the first control device 12 needs to adjust the control parameter to adjust the DO parameter within the DO target range.

At block S540, the first control device 12 adjusts the control parameter to adjust the plurality of operating parameters.

In at least one example embodiment, when the DO parameter is less than or equal to the DO lower threshold, the first control device 12 may increase the control parameter to increase the plurality of operating parameters. In other words, the operating frequencies and air volumes of the blowers 4411 and 4412 are increased through the frequency converters 4421 and 4422 . In the example embodiment, the first control device 12 may set the control parameter to a maximum aeration amount to maximize the plurality of operating parameters. In other words, the operating frequencies and air volumes of the plurality of blowers 4411 and 4412 are increased to the maximum frequency and the maximum air volume through the plurality of frequency converters 4421 and 4422 . In this exemplary embodiment, the maximum frequency and the maximum air volume may be the extreme values of the parameters of the frequency converters 4421 and 4422 and the blowers 4411 and 4412 in the parameter monitoring and water resource treatment method shown in FIG. 3 . .

In at least one example embodiment, when the DO parameter is greater than or equal to the DO upper threshold, the first control device 12 may decrease the control parameter to decrease the plurality of operating parameters. In other words, the operating frequencies and air volumes of the blowers 4411 and 4412 are reduced through the frequency converters 4421 and 4422 . In the example embodiment, the first control device 12 may set the control parameter to a minimum aeration amount to minimize the plurality of operating parameters. In other words, the operating frequencies and air volumes of the plurality of blowers 4411 and 4412 are increased to the minimum frequency and the minimum air volume through the plurality of frequency converters 4421 and 4422 . In this exemplary embodiment, the minimum frequency and the minimum air volume can be the extreme values of the parameters of the frequency converters 4421 and 4422 and the blowers 4411 and 4412 in the parameter monitoring and water resource treatment method shown in FIG. 3 .

At block S550, the first control device 12 confirms whether the first ORP parameter is greater than or equal to the ORP target value. When the first ORP parameter is less than the ORP target value, the method will proceed to block S561. When the first ORP parameter is greater than or equal to the ORP target value, the method will proceed to block S562.

In at least one example embodiment, the first control device 12 confirms the magnitude relationship between the first ORP parameter and the ORP target value, so as to confirm that the first ORP parameter is located at a first value greater than or equal to the ORP target value An ORP value area is also a second ORP value area that is less than the ORP target value. When the first ORP parameter is less than the ORP target value, the first ORP parameter is located in the second ORP value area. When the first ORP parameter is greater than or equal to the ORP target value, the first ORP parameter is located in the first ORP value area.

At block S561, the first control device 12 confirms whether a second ORP parameter at a second sensing time is smaller than the first ORP parameter. When the second ORP parameter is less than the first ORP parameter, the method will enter block S571. When the second ORP parameter is greater than or equal to the first ORP parameter, the method will proceed to block S572.

In at least one example embodiment, the first control device 12 may further receive the second ORP parameter sensed by the ORP sensing device at the second sensing moment. In this example embodiment, the second sensing time is later than the first sensing time, and the time difference between the second sensing time and the first sensing time may be equal to n×Δt. In this example embodiment, n is a positive integer.

In at least one example embodiment, the first control device 12 confirms the magnitude relationship between the first ORP parameter and the second ORP parameter to confirm that between the first sensing time and the second sensing time , the value of the ORP variable is increasing or decreasing. When the second ORP parameter is smaller than the first ORP parameter, the first ORP parameter is located in the second ORP value area and the value of the ORP variable is decreased. When the second ORP parameter is greater than or equal to the first ORP parameter, the first ORP parameter is located in the second ORP value area and the value of the ORP variable is increased.

At block S562, the first control device 12 confirms whether the second ORP parameter is greater than or equal to the first ORP parameter. When the second ORP parameter is smaller than the first ORP parameter, the party The method will proceed to block S572. When the second ORP parameter is greater than or equal to the first ORP parameter, the method will proceed to block S573.

In at least one example embodiment, the first control device 12 confirms the magnitude relationship between the first ORP parameter and the second ORP parameter to confirm that between the first sensing time and the second sensing time , the value of the ORP variable is increasing or decreasing. When the second ORP parameter is smaller than the first ORP parameter, the first ORP parameter is located in the first ORP value area and the value of the ORP variable is decreased. When the second ORP parameter is greater than or equal to the first ORP parameter, the first ORP parameter is located in the first ORP value area and the value of the ORP variable is increased.

At block S571, the first control device 12 generates a control coefficient Kp greater than 1 to control the control parameter.

In at least one example embodiment, since the first ORP parameter is lower than the ORP target value and the second ORP parameter is further lower than the first ORP parameter, the first control device 12 should attempt to turn up the control parameter to increase the corresponding plurality of operation parameters, thereby increasing the value of the ORP variable to the ORP target value. Therefore, the control coefficient Kp can be calculated as follows.

Kp=1+|[(ORP _target -ORP ₂ )]/ORP _target | Formula 1 wherein, the ORP _target is the ORP target value and the ORP ₂ is the second ORP parameter.

At block S572, the first control device 12 sets the control coefficient Kp equal to 1 to maintain the control parameter.

In at least one example embodiment, since the first ORP parameter is lower than the ORP target value, but the second ORP parameter is greater than or equal to the first ORP parameter, although the value of the ORP variable is lower than the ORP target value, However, there is still a situation in which the ORP target value is gradually approached, so the control coefficient Kp can be equal to 1 to maintain the control parameter.

In at least one example embodiment, since the first ORP parameter is greater than or equal to the ORP target value, but the second ORP parameter is less than the first ORP parameter, the ORP variable is higher than the ORP target value, but There is still a situation of gradually approaching the ORP target value, so the control coefficient Kp can be equal to 1 to maintain the control parameter.

At block S573, the first control device 12 generates the control coefficient Kp less than or equal to 1 to control the control parameter.

In at least one example embodiment, since the first ORP parameter is greater than or equal to the ORP target value, and the second ORP parameter is further greater than or equal to the first ORP parameter, the first control device 12 should attempt to turn down The control parameter is adjusted to lower the corresponding plurality of operation parameters, thereby reducing the value of the ORP variable to the ORP target value. Therefore, the control coefficient Kp can be calculated as follows.

Kp=1-|[(ORP _target -ORP ₂ )]/ORP _target | Formula 2 In this example embodiment, if the first ORP parameter is equal to the ORP target value and the second ORP parameter is further equal to the first ORP parameter ORP parameter, so the second ORP parameter is also equal to the ORP target value, then the control coefficient Kp is still equal to 1 after the operation of the formula 2.

At block S580, the first control device 12 adjusts the plurality of operating parameters according to the control parameter.

In at least one example embodiment, the first control device 12 can obtain the initial air volume of each of the plurality of blowers 4411 and 4412 through the plurality of operation relationships and the plurality of initial frequencies of the plurality of blowers 4411 and 4412 . The plurality of initial air volumes can be generated by using the plurality of initial frequencies and the plurality of operation relational expressions, and by formula 3 described below.

Q _BR,n =a _BR,n ×F _BR,n +b _BR,n Formula 3 Among them, Q _BR,n is the air volume of the nth blower, F _BR,n is the frequency of the nth blower and a _{BR, n} and b _BR,n are fixed parameters of the operational relationship of the nth blower.

In at least one example embodiment, the first control device 12 calculates a plurality of initial air volumes according to the plurality of initial frequencies of the plurality of blowers 4411 and 4412 and the plurality of fixed parameters, and calculates a plurality of initial air volumes through the plurality of initial air volumes. The summation yields an initial total air volume. In this exemplary embodiment, the first control device 12 multiplies the initial total air volume by the control coefficient Kp to generate an adjusted total air volume, that is, the adjusted aeration volume. In this exemplary embodiment, the first control device 12 further distributes the adjusted total air volume to the plurality of blowers 4411 and 4412 . For example, the first control device 12 evenly distributes the adjusted total air volume to the plurality of blowers 4411 and 4412 . In at least one example embodiment, the first control device 12 calculates a plurality of adjustment frequencies respectively according to the plurality of distributed air volumes and the plurality of fixed parameters, as the operation parameters of the plurality of blowers 4411 and 4412, and through the plurality of blowers 4411 and 4412 The frequency converters 4421 and 4422 adjust the operating frequencies of the blowers 4411 and 4412 to the operating parameters.

Please refer to FIG. 6 . FIG. 6 is a schematic diagram of the nitrifying liquid internal reflux control system 6 of the water resource monitoring and treatment system 1 according to an exemplary embodiment of the present invention. The nitrifying liquid internal reflux control system 6 includes a first control device 12 , a plurality of sensing devices 611 , 612 and 613 , a frequency converter 642 and an internal reflux pump 643 . In this exemplary embodiment, the nitrifying solution internal reflux control system 6 measures the water body 10 to be measured through the plurality of sensing devices 611 , 612 and 613 to obtain a plurality of sensing parameters. In the exemplary embodiment, the first control device 12 uses the acquired sensing parameters to generate the first control parameters through the first operation mode, and generates the first control parameters according to the first control parameters. The internal return pump 643 and the frequency converter 642 are regulated.

In at least one example embodiment, the nitrification solution internal reflux control system 6 further has the second control device 13 shown in FIG. 1 , and the second control device 13 also uses the acquired sensing parameters to The second operation mode generates a plurality of second control parameters, and the plurality of second control parameters can be used to replace the plurality of first control parameters to regulate the internal return pump 643 and the frequency converter 642 .

In at least one example embodiment, the plurality of sensing devices 611, 612, and 613 may be the ORP sensing device, the MLSS sensing device, and the pH sensing device, respectively. In this example embodiment, both the internal return pump 643 and the frequency converter 642 are one of the plurality of controlled devices described in FIG. 1 .

In at least one example embodiment, the water body 10 to be tested can be distributed in an anaerobic tank 601 , an anoxic tank 602 and an aerobic tank 603 . In this exemplary embodiment, the anaerobic tank 601 , the anoxic tank 602 and the aerobic tank 603 can be used for biological denitrification and phosphorus removal of the water body 10 to be tested. In this example embodiment, the plurality of sensing devices 611 , 612 and 613 may be disposed in the hypoxia tank 602 .

In at least one example embodiment, the nitrifying liquid internal return control system 6 may additionally have a sludge return pump 644 . In this example embodiment, the sludge return pump 644 is used in a settling tank 604 located after the aerobic tank 603 .

Please refer to FIG. 7 , which is a schematic diagram of a sludge return control system 7 of the water resource monitoring and treatment system 1 according to an exemplary embodiment of the present invention. The sludge return control system 7 includes a first control device 12 , a plurality of sensing devices 711 , 712 and 713 , a frequency converter 742 and a sludge return pump 744 . In this exemplary embodiment, the sludge return control system 7 measures the water body 10 to be measured through the plurality of sensing devices 711 , 712 and 713 to obtain a plurality of sensing parameters. In the exemplary embodiment, the first control device 12 uses the acquired sensing parameters to generate the first control parameters through the first operation mode, and generates the first control parameters according to the first control parameters. The sludge return pump 744 and the frequency converter 742 are regulated.

In at least one example embodiment, the sludge return control system 7 further has the second control device 13 shown in FIG. Two operation modes to generate a plurality of second control parameters, the plurality of second control parameters It can be used to replace the plurality of first control parameters to regulate the sludge return pump 744 and the frequency converter 742 .

In at least one example embodiment, the plurality of sensing devices 711, 712, and 713 may be the ORP sensing device, the MLSS sensing device, and the pH sensing device, respectively. In this example embodiment, both the sludge return pump 744 and the frequency converter 742 are one of the plurality of controlled devices described in FIG. 1 .

In at least one example embodiment, the water body 10 to be tested can be distributed in an anaerobic tank 701 , an anoxic tank 702 and an aerobic tank 703 . In this exemplary embodiment, the anaerobic tank 701 , the anoxic tank 702 and the aerobic tank 703 can be used for biological denitrification and phosphorus removal of the water body 10 to be tested. In this example embodiment, the plurality of sensing devices 711 , 712 and 713 may be disposed in the anaerobic tank 701 .

In at least one example embodiment, the sludge return pump 744 is used in a settling tank 704 located after the aerobic tank 703 .

FIG. 8 shows a flow chart of the backflow control process in the parameter monitoring and water resource treatment method of the exemplary embodiment of the present invention. Because of the variety of ways in which the exemplary method can be performed, the method is provided by way of example only. The methods described below may be performed using, for example, the systems and apparatus shown in FIGS. 1, 2, and 6 or 7, and exemplary methods are explained with reference to various elements of these figures. Each block shown in FIG. 8 represents one or more routines, methods, or subroutines performed in the exemplary method. Furthermore, the order of the blocks is exemplary only and may vary. Additional blocks may be added or fewer blocks may be used without departing from the invention. In addition, FIG. 8 is a flowchart of a specific implementation method of the parameter monitoring and water resource treatment method shown in FIG. 3 .

In at least one example embodiment, the backflow control process may be an internal backflow control process of the nitrification solution performed by the backflow control system 6 in the nitrification solution shown in FIG. 6 . implemented in at least one example In the manner, the backflow control process may be a sludge backflow control process performed by the sludge backflow control system 7 of FIG. 7 .

At block S810, the first control device 12 obtains an ORP target value and an MLSS target range.

In at least one example embodiment, the first control device 12 obtains the ORP target value in a predetermined manner. In the exemplary embodiment, the preset mode can directly read the ORP target value from the internal storage 222 through the first control device 12 . In another exemplary embodiment, the predetermined manner may generate the ORP target value through the first control device 12 through an ORP target value determination method.

In at least one example embodiment, the first control device 12 obtains the MLSS target range through the preset method. In the exemplary embodiment, the preset mode can directly read the MLSS target range from the internal storage 222 through the first control device 12 . In this example embodiment, the MLSS target range includes a lower MLSS threshold and an upper MLSS threshold.

In at least one example embodiment, the first control device 12 obtains the serial number, operation relationship, minimum frequency, maximum frequency and initial frequency of the internal return pump 643 . In at least one example embodiment, when the number of the internal return pumps 643 exceeds one, the first control device 12 obtains the respective numbers, operation relational expressions, minimum frequency, maximum frequency and initial frequency.

In at least one example embodiment, the first control device 12 obtains the serial number, operation relationship, minimum frequency, maximum frequency and initial frequency of the sludge return pump 744 . In at least one example embodiment, when the number of the sludge return pumps 744 exceeds one, the first control device 12 obtains the respective numbers, operation relational expressions, minimum frequency, maximum frequency of the multiple sludge return pumps 744 frequency and initial frequency.

At block S820, the first control device 12 receives a first ORP parameter sensed by the ORP sensing device at a first sensing moment and a first ORP parameter sensed by the MLSS sensing device at the first sensing moment A DO parameter.

In at least one example embodiment, the ORP sensing device and the MLSS sensing device can periodically sense the ORP parameter and the MLSS parameter of the water body 10 to be tested, and can also continuously sense the water body 10 to be tested. The ORP parameter and the MLSS parameter. In the exemplary embodiment, the first control device 12 may have a control period Δt in the time interval between each time obtaining the ORP variable and the value of the MLSS variable.

At block S830, the first control device 12 confirms whether the MLSS parameter is within the MLSS target range. When the MLSS parameter is not within the MLSS target range, the method will proceed to block S840. When the MLSS parameter is within the MLSS target range, the method will proceed to block S850.

In at least one example embodiment, when the MLSS parameter is not within the MLSS target range, the first control device 12 needs to adjust the control parameter to adjust the MLSS parameter within the MLSS target range.

At block S840, the first control device 12 adjusts the control parameter to adjust the plurality of operating parameters.

In at least one example embodiment, when the MLSS parameter is less than or equal to the MLSS lower threshold, the first control device 12 may increase the control parameter to increase the plurality of operating parameters. In other words, the operation frequency and flow rate of the internal return pump 643 are increased through the frequency converter 642 or the operation frequency and flow rate of the sludge return pump 744 are increased through the frequency converter 742 . In the example embodiment, the first control device 12 may set the control parameter to a maximum internal return flow to maximize the plurality of operating parameters. In other words, through the frequency converter 642, the operating frequency and the return flow of the internal return pump 643 are increased to the maximum frequency and maximum return flow. In another example implementation , the first control device 12 may set the control parameter to the maximum sludge return flow to maximize the plurality of operating parameters. In other words, the operating frequency and the return flow of the sludge return pump 744 are increased to the maximum frequency and maximum return flow through the frequency converter 742 . In this exemplary embodiment, the maximum frequency and the maximum return flow can be the frequency converters 642 and 742 , the internal return pump 643 and the sludge return pump 744 in the parameter monitoring and water resource treatment method shown in FIG. 3 . parameter extremes.

In at least one example embodiment, when the MLSS parameter is greater than or equal to the MLSS upper threshold, the first control device 12 may decrease the control parameter to decrease the plurality of operating parameters. In other words, the operation frequency and flow rate of the internal return pump 643 are reduced through the frequency converter 642 or the operation frequency and flow rate of the sludge return pump 744 are reduced through the frequency converter 742 . In the example embodiment, the first control device 12 may set the control parameter to a minimum internal return flow to minimize the plurality of operating parameters. In other words, through the frequency converter 642, the operating frequency and the return flow of the internal return pump 643 are reduced to the minimum frequency and the minimum return flow. In another example embodiment, the first control device 12 may set the control parameter to a minimum sludge return to minimize the plurality of operating parameters. In other words, the operating frequency and the return flow of the sludge return pump 744 are reduced to the minimum frequency and minimum return flow through the frequency converter 742 . In this exemplary embodiment, the minimum frequency and the minimum return flow can be the frequency converters 642 and 742 , the internal return pump 643 and the sludge return pump 744 in the parameter monitoring and water resources treatment method shown in FIG. 3 parameter extremes.

At block S850, the first control device 12 confirms whether the first ORP parameter is greater than or equal to the ORP target value. When the first ORP parameter is less than the ORP target value, the method will proceed to block S861. When the first ORP parameter is greater than or equal to the ORP target value, the method will proceed to block S862.

In at least one example embodiment, the first control device 12 confirms the magnitude relationship between the first ORP parameter and the ORP target value, so as to confirm that the first ORP parameter is located at a first value greater than or equal to the ORP target value An ORP value range is also a first value less than the ORP target value Two ORP numerical fields. When the first ORP parameter is less than the ORP target value, the first ORP parameter is located in the second ORP value area. When the first ORP parameter is greater than or equal to the ORP target value, the first ORP parameter is located in the first ORP value area.

At block S861, the first control device 12 confirms whether a second ORP parameter at a second sensing time is smaller than the first ORP parameter. When the second ORP parameter is less than the first ORP parameter, the method will proceed to block S871. When the second ORP parameter is greater than or equal to the first ORP parameter, the method will proceed to block S872.

In at least one example embodiment, the first control device 12 may further receive the second ORP parameter sensed by the ORP sensing device at the second sensing moment. In this example embodiment, the second sensing time is later than the first sensing time, and the time difference between the second sensing time and the first sensing time may be equal to n×Δt. In this example embodiment, n is a positive integer.

In at least one example embodiment, the first control device 12 confirms the magnitude relationship between the first ORP parameter and the second ORP parameter to confirm that between the first sensing time and the second sensing time , the value of the ORP variable is increasing or decreasing. When the second ORP parameter is smaller than the first ORP parameter, the first ORP parameter is located in the second ORP value area and the value of the ORP variable is decreased. When the second ORP parameter is greater than or equal to the first ORP parameter, the first ORP parameter is located in the second ORP value area and the value of the ORP variable is increased.

At block S862, the first control device 12 confirms whether the second ORP parameter is greater than or equal to the first ORP parameter. When the second ORP parameter is less than the first ORP parameter, the method will proceed to block S872. When the second ORP parameter is greater than or equal to the first ORP parameter, the method will proceed to block S873.

In at least one example embodiment, the first control device 12 confirms the magnitude relationship between the first ORP parameter and the second ORP parameter to confirm that between the first sensing time and the second sensing time , the value of the ORP variable is increasing or decreasing. When the second ORP parameter is smaller than the first ORP parameter, the first ORP parameter is located in the first ORP value area and the value of the ORP variable is decreased. When the second ORP parameter is greater than or equal to the first ORP parameter, the first ORP parameter is located in the first ORP value area and the value of the ORP variable is increased.

At block S871, the first control device 12 generates a control coefficient Kp greater than 1 to control the control parameter.

In at least one example embodiment, since the first ORP parameter is lower than the ORP target value and the second ORP parameter is further lower than the first ORP parameter, the first control device 12 should attempt to turn up the control parameter to increase the corresponding plurality of operation parameters, thereby increasing the value of the ORP variable to the ORP target value. Therefore, the control coefficient Kp can be calculated by the aforementioned formula 1.

At block S872, the first control device 12 sets the control coefficient Kp equal to 1 to maintain the control parameter.

In at least one example embodiment, since the first ORP parameter is lower than the ORP target value, but the second ORP parameter is greater than or equal to the first ORP parameter, although the value of the ORP variable is lower than the ORP target value, However, there is still a situation in which the ORP target value is gradually approached, so the control coefficient Kp can be equal to 1 to maintain the control parameter.

In at least one example embodiment, since the first ORP parameter is greater than or equal to the ORP target value, but the second ORP parameter is less than the first ORP parameter, the ORP variable is higher than the ORP target value, but There is still a situation of gradually approaching the ORP target value, so the control coefficient Kp can be equal to 1 to maintain the control parameter.

At block S873, the first control device 12 generates the control coefficient Kp less than or equal to 1 to control the control parameter.

In at least one example embodiment, since the first ORP parameter is greater than or equal to the ORP target value, and the second ORP parameter is further greater than or equal to the first ORP parameter, the first control device 12 should attempt to turn down The control parameter is adjusted to lower the corresponding plurality of operation parameters, thereby reducing the value of the ORP variable to the ORP target value. Therefore, the control coefficient Kp can be calculated by the aforementioned formula 2. In this example embodiment, if the first ORP parameter is equal to the ORP target value and the second ORP parameter is further equal to the first ORP parameter, so the second ORP parameter is also equal to the ORP target value, then the regulation coefficient Kp is still equal to 1 after the operation of the formula 2.

At block S880, the first control device 12 adjusts the plurality of operating parameters according to the control parameter.

In at least one example embodiment, the first control device 12 can obtain the initial flow rate of the internal return pump 643 through the operation relationship and the initial frequency of the internal return pump 643 . The initial flow can be generated by using the initial frequency and the operating relationship, and by formula 4 described below. .

Q _INRP =a _INRP,n ×F _INRP,n +b _INRP,n Formula 4 Where, Q _INRP,n is the flow rate of the nth internal return pump, F _INRP,n is the frequency of the nth internal return pump and a _INRP,n and b _INRP,n are fixed parameters of the operational relationship of the nth internal return pump.

In at least one example embodiment, the first control device 12 calculates the initial flow according to the initial frequency of the internal return pump 643 and the plurality of fixed parameters, and generates the initial flow through the product of the initial flow and the control coefficient Kp. By adjusting the flow, the adjusted internal return flow can be known. In at least one example embodiment, the first control device 12 uses the internal return flow and the multiple A fixed parameter is calculated, and the adjustment frequency is calculated as the operating parameter of the internal return pump 643 , and the operating frequency of the internal return pump 643 is adjusted to the operating parameter through the frequency converter 642 .

In at least one example embodiment, when the number of the internal return pumps 643 exceeds one, the first control device 12 calculates a plurality of initial frequencies and a plurality of fixed parameters of the plurality of internal return pumps respectively. initial flow, and an initial total flow is generated by summing the plurality of initial flows. In this example embodiment, the first control device 12 multiplies the initial total flow rate by the regulation coefficient Kp to generate an adjusted total flow rate, that is, the adjusted internal return flow rate. In the example embodiment, the first control device 12 then distributes the adjusted internal return flow to the plurality of internal return pumps. For example: the first control device 12 equally distributes the adjusted total flow to the plurality of internal return pumps. In at least one example embodiment, the first control device 12 calculates a plurality of adjustment frequencies according to the plurality of distributed flow rates and the plurality of fixed parameters, respectively, as the operation parameters of the plurality of internal return pumps.

In at least one example embodiment, the first control device 12 can obtain the initial flow rate of the sludge return pump 744 through the operation relationship and the initial frequency of the sludge return pump 744 . The initial flow rate can be generated by using the initial frequency and the operating relationship, and by formula 5 described below.

Q _SP =a _SP,n ×F _SP,n +b _SP, nFormula 5 Among them, Q _SP,n is the flow rate of the nth sludge return pump, and F _SP,n is the frequency of the nth sludge return pump And a _SP,n and b _SP,n are the fixed parameters of the operation relationship of the nth sludge return pump.

In at least one example embodiment, the first control device 12 calculates the initial flow rate according to the initial frequency of the sludge return pump 744 and the plurality of fixed parameters, and calculates the initial flow rate through the product of the initial flow rate and the control coefficient Kp. The adjusted flow rate is generated, that is, the adjusted sludge return flow rate can be known. In at least one example embodiment, the first control device 12 uses the sludge return amount and the multiple A fixed parameter is calculated, and the adjustment frequency is calculated as the operation parameter of the sludge return pump 744 , and the operation frequency of the sludge return pump 744 is adjusted to the operation parameter through the frequency converter 742 .

In at least one example embodiment, when the number of the sludge return pumps 744 exceeds one, the first control device 12 calculates the respective initial frequencies of the sludge return pumps and the fixed parameters according to the multiple initial frequencies and the multiple fixed parameters. A plurality of initial flows, and an initial total flow is generated by summing the plurality of initial flows. In this exemplary embodiment, the first control device 12 multiplies the initial total flow by the control coefficient Kp to generate an adjusted total flow, that is, the adjusted sludge return flow. In the example embodiment, the first control device 12 then distributes the adjusted sludge return flow rate to the plurality of sludge return pumps. For example, the first control device 12 evenly distributes the adjusted total flow to the plurality of sludge return pumps. In at least one example embodiment, the first control device 12 calculates a plurality of adjustment frequencies according to the plurality of distribution flow rates and the plurality of fixed parameters, respectively, as the operation parameters of the plurality of sludge return pumps.

Please refer to FIG. 9 , which is a schematic diagram of the intermittent aeration control system 9 of the water resource monitoring and treatment system 1 according to an exemplary embodiment of the present invention. The intermittent aeration control system 9 includes a first control device 12 , a plurality of sensing devices 911 , 912 , 913 and 914 , a blower 941 , a frequency converter 942 and a flow propeller 945 . In this exemplary embodiment, the intermittent aeration control system 9 measures the water body 10 to be measured through the plurality of sensing devices 911 , 912 , 913 and 914 to obtain a plurality of sensing parameters. In the exemplary embodiment, the first control device 12 uses the acquired sensing parameters to generate the first control parameters through the first operation mode, and generates the first control parameters according to the first control parameters. The blower 941 , the frequency converter 942 and the flow propeller 945 are regulated and controlled.

In at least one example embodiment, the intermittent aeration control system 9 further has the second control device 13 shown in FIG. 1 , and the second control device 13 also uses the acquired sensing parameters to Two operation modes to generate a plurality of second control parameters, the plurality of second control parameters It can be used to replace the plurality of first control parameters to regulate the blower 941 , the frequency converter 942 and the flow pusher 945 .

In at least one example embodiment, the plurality of sensing devices 911, 912, 913, and 914 may be the ORP sensing device, the MLSS sensing device, the DO sensing device, and the pH sensing device, respectively. In this exemplary embodiment, the blower 941 , the frequency converter 942 and the flow pusher 945 are all one of the plurality of controlled devices 14 described in FIG. 1 .

In at least one example embodiment, the water body 10 to be tested may be distributed in an aerobic tank 903 . In this exemplary embodiment, the aerobic tank 903 can be used for the biological denitrification and dephosphorization of the water body 10 to be tested. In this example embodiment, the plurality of sensing devices 911 , 912 , 913 and 914 may be disposed in the aerobic tank 901 .

FIG. 10 shows a flow chart of the intermittent aeration control process in the parameter monitoring and water resource treatment method of the exemplary embodiment of the present invention. Because of the variety of ways in which the exemplary method can be performed, the method is provided by way of example only. The methods described below may be performed using, for example, the systems and apparatus shown in FIGS. 1, 2, and 9, and exemplary methods are explained with reference to various elements of these figures. Each block shown in FIG. 10 represents one or more routines, methods, or subroutines performed in the exemplary method. Furthermore, the order of the blocks is exemplary only and may vary. Additional blocks may be added or fewer blocks may be used without departing from the invention. In addition, FIG. 10 is a flowchart of a specific implementation method of the parameter monitoring and water resource treatment method shown in FIG. 3 .

At block S1010, the first control device 12 obtains an ORP target range and an MLSS target range.

In at least one example embodiment, the first control device 12 obtains the ORP target range in a predetermined manner. In the exemplary embodiment, the preset mode can directly read the ORP target range from the internal storage 222 through the first control device 12 . In another exemplary embodiment, the preset mode can be determined by an ORP target value through the first control device 12 method to generate the ORP target range. In the example embodiment, the ORP target range includes a lower ORP threshold and an upper ORP threshold. In this example embodiment, the ORP lower threshold is the non-aeration phase ORP control target value, and the ORP upper threshold is the aeration phase ORP control target value.

In at least one example embodiment, the first control device 12 obtains the MLSS target range through the preset method. In the exemplary embodiment, the preset mode can directly read the MLSS target range from the internal storage 222 through the first control device 12 . In this example embodiment, the MLSS target range includes a lower MLSS threshold and an upper MLSS threshold.

In at least one example embodiment, the first control device 12 obtains the number, operation relationship, minimum frequency, maximum frequency and initial frequency of the blower 941 . In at least one example embodiment, when the number of the blowers 941 exceeds one, the first control device 12 obtains the respective numbers, operation relational expressions, minimum frequency, maximum frequency and initial frequency of the plurality of blowers 941 .

At block S1020, the first control device 12 receives a first ORP parameter sensed by the ORP sensing device 911 at a first sensing moment and the MLSS sensing device 912 sensed at the first sensing moment to an MLSS parameter.

In at least one example embodiment, the ORP sensing device 911 and the MLSS sensing device 912 can periodically sense the ORP parameter and the MLSS parameter of the water body 10 to be measured, and can also continuously sense the water body to be measured 10 of the ORP parameter and the MLSS parameter. In the exemplary embodiment, the first control device 12 may have a control period Δt in the time interval between each time obtaining the ORP variable and the value of the MLSS variable.

At block S1030, the first control device 12 confirms whether the MLSS parameter is within the MLSS target range. When the MLSS parameter is not within the MLSS target range, the method will Go to block S1040. When the MLSS parameter is within the MLSS target range, the method will proceed to block S1050.

In at least one example embodiment, when the MLSS parameter is not within the MLSS target range, the first control device 12 needs to adjust the control parameter to adjust the MLSS parameter within the MLSS target range.

At block S1040, the first control device 12 adjusts the control parameter to adjust the plurality of operating parameters.

In at least one example embodiment, when the MLSS parameter is less than or equal to the MLSS lower threshold, the first control device 12 may lower the control parameter to lower the plurality of operating parameters. In other words, the operating frequency and air volume of the blower 941 are reduced through the frequency converter 942 . In the example embodiment, the first control device 12 may set the control parameter to a minimum aeration amount to minimize the plurality of operating parameters. In other words, through the inverter 942, the operating frequency and air volume of the blower 941 are reduced to the minimum frequency and the minimum air volume, thereby increasing the value of the MLSS variable. In this exemplary embodiment, the minimum frequency and the minimum air volume can be used by the inverter 942 to increase the parameter extreme values of the blower 941 in the parameter monitoring and water resource treatment method shown in FIG. 3 .

In at least one example embodiment, when the MLSS parameter is greater than or equal to the MLSS upper threshold, the first control device 12 may increase the control parameter to increase the plurality of operating parameters. In other words, the operating frequency and air volume of the blower 941 are increased through the frequency converter 942 . In the example embodiment, the first control device 12 may set the control parameter to a maximum aeration amount to maximize the plurality of operating parameters. In other words, the operating frequency and air volume of the blower 941 are increased to the maximum frequency and the maximum air volume through the inverter 942, thereby reducing the value of the MLSS variable. In this exemplary embodiment, the maximum frequency and the maximum air volume can increase the parameter extreme values of the blower 941 for the inverter 942 in the parameter monitoring and water resource treatment method shown in FIG. 3 .

At block S1050, the first control device 12 confirms whether the first ORP parameter is within the ORP target range. When the first ORP parameter is within the ORP target range, the method will proceed to block S1060. When the first ORP parameter is not within the ORP target range, the method will proceed to block S1082.

In at least one example embodiment, the first control device 12 confirms the magnitude relationship between the first ORP parameter and the ORP target range, so as to confirm that the first ORP parameter is located at a first value greater than or equal to the ORP upper limit threshold An ORP value area, or a second ORP value area that is less than the ORP upper threshold and greater than the ORP lower threshold, or a third ORP value area that is less than or equal to the ORP lower threshold.

At block S1060, the first control device 12 confirms whether a second ORP parameter at a second sensing time is less than or equal to the first ORP parameter. When the second ORP parameter is greater than the first ORP parameter, the method will enter block S1071. When the second ORP parameter is less than or equal to the first ORP parameter, the method will proceed to block S1072.

In at least one example embodiment, the first control device 12 may further obtain the second ORP parameter sensed by the ORP sensing device at the second sensing moment. In this example embodiment, the second sensing moment is earlier than the first sensing moment, so the second ORP parameter may be received by the first control device 12 before the first ORP parameter is received. In this example embodiment, the time difference between the second sensing instant and the first sensing instant may be equal to n×Δt. In this example embodiment, n is a positive integer.

In at least one example embodiment, the first control device 12 confirms the magnitude relationship between the first ORP parameter and the second ORP parameter to confirm that between the first sensing time and the second sensing time , the value of the ORP variable is increasing or decreasing. When the second ORP parameter is less than or equal to the first ORP parameter, the first ORP parameter is located in the second ORP value area and the value of the ORP variable is increased. When the second ORP parameter is greater than the first ORP parameter , the first ORP parameter is located in the second ORP value area and the value of the ORP variable is decreased.

At block S1071, the first control device 12 generates a control coefficient Kp greater than 1 to control the control parameter.

In at least one example embodiment, since the second ORP parameter is greater than the first ORP parameter, the first control device 12 should attempt to increase the control parameter to increase the corresponding operating parameters, thereby avoiding The value of the ORP variable continued to decrease. Therefore, the control coefficient Kp can be calculated as follows.

Kp=1+|[(ORP _target -ORP ₁ )]/ORP _target | Formula 6 wherein, the ORP _target is the ORP upper limit threshold and the ORP ₁ is the first ORP parameter.

At block S1072, the first control device 12 sets the control coefficient Kp equal to 1 to maintain the control parameter.

In at least one example embodiment, since the second ORP parameter is less than or equal to the first ORP parameter, it represents that the value of the ORP variable continues to increase to maintain the operation of the nitrification reaction, so the first control device 12 can maintain the control parameter, so the control coefficient Kp can be equal to 1 to maintain the control parameter.

At block S1081, the first control device 12 adjusts the plurality of operating parameters according to the control parameters.

In at least one example embodiment, the first control device 12 can obtain the initial air volume of the blower 941 through the operation relationship and the initial frequency of the blower 941 . The initial air volume can be generated by using the initial frequency and the operating relationship, and by the aforementioned formula 3.

In at least one example embodiment, the first control device 12 calculates the initial air volume according to the initial frequency of the blower 941 and the plurality of fixed parameters, and generates the adjusted flow through the product of the initial air volume and the control coefficient Kp. , you can know the adjusted air volume. at least one In the exemplary embodiment, the first control device 12 calculates the adjustment frequency by using the adjusted air volume and the plurality of fixed parameters as the operation parameter of the blower 941, and adjusts the operation frequency of the blower 941 through the frequency converter 942. to the operating parameter.

In at least one example embodiment, when the number of the blowers 941 exceeds one, the first control device 12 calculates a plurality of initial air volumes according to the plurality of initial frequencies and the plurality of fixed parameters of the plurality of blowers, and An initial total air volume is generated through the summation of the plurality of initial air volumes. In this exemplary embodiment, the first control device 12 multiplies the initial total air volume by the control coefficient Kp to generate an adjusted total air volume, that is, the adjusted aeration volume. In the example embodiment, the first control device 12 then distributes the adjusted total air volume to the plurality of blowers. For example, the first control device 12 evenly distributes the adjusted total air volume to the plurality of blowers. In at least one example embodiment, the first control device 12 calculates a plurality of adjustment frequencies respectively by using the plurality of distributed air volumes and the plurality of fixed parameters as the operation parameters of the plurality of blowers.

In at least one example embodiment, the first control device 12 may not activate the flow propeller 945 additionally, so as to maintain the operating frequency of the flow propeller 945 at 0, so as to avoid the loss of aeration amount due to the operation of the flow propeller 945 .

At block S1082, the first control device 12 sets the control parameter to adjust the plurality of operating parameters.

In at least one example embodiment, when the first ORP parameter is less than or equal to the ORP lower threshold, it means that the current value of the ORP variable is too low, so the first control device 12 should try to increase the control parameter to increase the corresponding the plurality of operating parameters, thereby increasing the value of the ORP variable to return to the ORP target range. In other words, the operating frequency and air volume of the blower 941 can be increased through the frequency converter 942 . In the example embodiment, the first control device 12 may set the control parameter to a maximum aeration amount to maximize the plurality of operating parameters. In other words, through the inverter 942, the operating frequency and air volume of the blower 941 can be increased to the maximum frequency and maximum air volume. High air volume. In this exemplary embodiment, the first control device 12 may not activate the flow propeller 945 additionally, so as to maintain the operating frequency of the flow propeller 945 at 0, so as to avoid the loss of aeration volume due to the operation of the flow propeller 945 .

In at least one example embodiment, when the first ORP parameter is greater than or equal to the ORP upper limit threshold, it means that the current value of the ORP variable is too high, so the first control device 12 should try to reduce the control parameter to reduce the corresponding operating parameters, thereby reducing the value of the ORP variable to return within the ORP target range. In other words, the operating frequency and air volume of the blower 941 can be reduced through the frequency converter 942 . In the example embodiment, the first control device 12 may set the control parameter to a minimum aeration amount to minimize the plurality of operating parameters. In other words, the operating frequency and air volume of the blower 941 can be reduced to the minimum frequency and the minimum air volume through the frequency converter 942 . In this exemplary embodiment, the first control device 12 can additionally activate the flow propeller 945, increase the operating frequency of the flow propeller 945, and add the operation of the flow propeller 945 to reduce the aeration volume. In this exemplary embodiment, the operating frequency of the flow pusher 945 can be set to a predetermined frequency within a predetermined interval. The preset frequency may be 60 hertz (Hz).

Please refer to FIG. 11 . FIG. 11 is a schematic diagram of a segmented inflow control system 110 of the water resource monitoring and treatment system 1 according to an exemplary embodiment of the present invention. The segmented inflow control system 110 includes a first control device 12, a plurality of sensing devices 11111-11113 and 11121-11123, and a plurality of gate valves 11461-11463. In this exemplary embodiment, the segmented inflow control system 110 measures the water body 10 to be measured through the plurality of sensing devices 11111-11113 and 11121-11123 to obtain a plurality of sensing parameters. In the exemplary embodiment, the first control device 12 uses the acquired sensing parameters to generate the first control parameters through the first operation mode, and generates the first control parameters according to the first control parameters. The plurality of gate valves 11461-11463 are regulated.

In at least one example embodiment, the segmented inflow control system 110 further includes the second control device 13 shown in FIG. 1 , and the second control device 13 also utilizes the acquired senses A plurality of second control parameters are generated through the second operation mode, and the plurality of second control parameters can be used to replace the plurality of first control parameters to regulate the plurality of gate valves 11461-11463.

In at least one example embodiment, the plurality of sensing devices 11111-11113 can be the plurality of ORP sensing devices, and the plurality of sensing devices 11121-11123 can be the plurality of pH sensing devices. In this example embodiment, the plurality of gate valves 11461-11463 are all one of the plurality of controlled devices described in FIG. 1 .

In at least one example embodiment, the water body 10 to be tested can be distributed in a plurality of anoxic tanks 11021-11023 and a plurality of aerobic tanks 11031-11033. In this exemplary embodiment, the multiple anoxic tanks 11021-11023 and the multiple aerobic tanks 11031-11033 can be used to perform biological nitrogen and phosphorus removal operations on the water body 10 to be tested. In this example embodiment, the plurality of sensing devices 11111-11113 and 11121-11123 may be disposed in the plurality of anoxic cells 11021-11023.

In at least one example embodiment, the gate valve 11461, the ORP sensing device 11111 and the pH sensing device 11121 can be disposed in the anoxic tank 11021, the gate valve 11462, the ORP sensing device 11112 and the pH sensing device 11021 The measuring device 11122 can be disposed in the anoxic tank 11022 , and the gate valve 11461 , the ORP sensing device 11113 and the pH sensing device 11123 can be disposed in the anoxic tank 11023 .

FIG. 12 shows a flow chart of the segmented inflow control process in the parameter monitoring and water resources processing method of the exemplary embodiment of the present invention. Because of the variety of ways in which the exemplary method can be performed, the method is provided by way of example only. The methods described below may be performed using, for example, the systems and apparatus shown in FIGS. 1, 2, and 11, and exemplary methods are explained with reference to various elements of these figures. Each block shown in FIG. 12 represents one or more routines, methods, or subroutines performed in the exemplary method. Furthermore, the order of the blocks is exemplary only and may vary. Additional blocks may be added or fewer blocks may be used without departing from the invention. In addition, FIG. 12 is a flowchart of a specific implementation method of the parameter monitoring and water resource treatment method shown in FIG. 3 .

At block S1210, the first control device 12 obtains a plurality of ORP target values.

In at least one example embodiment, the first control device 12 obtains the ORP target values in a predetermined manner. In the exemplary embodiment, the preset mode can directly read the ORP target values from the internal storage 222 through the first control device 12 . In another exemplary embodiment, the predetermined manner may generate the plurality of ORP target values through the first control device 12 through an ORP target value determination method. In at least one example embodiment, the ORP target values correspond to different hypoxia pools 11021-11023 on a one-to-one basis.

In at least one example embodiment, the first control device 12 obtains the respective numbers, operating relationship, minimum orifice opening, maximum orifice opening, and initial orifice opening of the plurality of gate valves 11461-11463.

At block S1220, the first control device 12 receives a plurality of first ORP parameters sensed by the plurality of ORP sensing devices at a first sensing moment.

In at least one example embodiment, the ORP sensing devices 11111-11113 can periodically sense the ORP parameters of the water body 10 to be measured, and can also continuously sense the value of the ORP variables of the water body 10 to be measured. . In this exemplary embodiment, the first control device 12 may have a control period Δt in the time interval between each acquisition of the value of the ORP variable.

At block S1230, the first control device 12 compares the plurality of first ORP parameters with the plurality of ORP target values to generate a plurality of first comparison results.

In at least one example embodiment, each of the plurality of first ORP parameters is compared with a corresponding one of the plurality of ORP target values to generate a corresponding one of the plurality of first comparison results. In this example embodiment, the first ORP parameter generated by the ORP sensing device 11111 in the hypoxia pool 11021 is compared with the first ORP target value corresponding to the hypoxia pool 11021 to generate the first ORP parameter. A first comparison result. In addition, the first ORP parameter generated by the ORP sensing device 11112 in the hypoxia pool 11022 will be matched with the hypoxia pool 11022 The first ORP target value should be compared to produce a second first comparison result. Furthermore, the first ORP parameter generated by the ORP sensing device 11113 in the hypoxia pool 11023 is compared with the first ORP target value corresponding to the hypoxia pool 11023 to generate a third first ORP value. Comparing results.

At block S1240, the first control device 12 compares the plurality of second ORP parameters sensed from the plurality of ORP sensing devices at a second sensing moment with the plurality of first ORP parameters to generate A plurality of second comparison results are generated, and a plurality of control parameters are generated according to the plurality of first comparison results and the plurality of second comparison results.

In at least one example embodiment, the first control device 12 may further obtain the plurality of second ORP parameters sensed by the plurality of ORP sensing devices 11111-11113 at the second sensing time. In this example embodiment, the second sensing time is earlier than the first sensing time, so the plurality of second ORP parameters may be obtained before the first control device 12 receives the plurality of first ORP parameters received. In this example embodiment, the time difference between the second sensing instant and the first sensing instant may be equal to n×Δt. In this example embodiment, n is a positive integer.

In at least one example embodiment, the first control device 12 confirms the magnitude relationship between the plurality of first ORP parameters and the plurality of second ORP parameters, so as to confirm the relationship between the first sensing moment and the second sensing time. Between the measurement times, the values of the ORP variables are increased or decreased, and the plurality of second comparison results are correspondingly generated. When one of the plurality of second ORP parameters is greater than the corresponding one of the plurality of first ORP parameters, the value of the ORP variable of the corresponding one of the plurality of anoxic pools 11021-11023 is increased. When one of the plurality of second ORP parameters is less than or equal to a corresponding one of the plurality of first ORP parameters, the value of the ORP variable of the corresponding one of the plurality of anoxic pools 11021-11023 is decreased.

In at least one example embodiment, when one of the plurality of first comparison results shows that a corresponding one of the plurality of first ORP parameters is greater than or equal to a corresponding one of the plurality of ORP target values When the corresponding one of the plurality of second comparison results shows that the corresponding one of the plurality of second ORP parameters is less than or equal to the corresponding first ORP parameter, the first control device 12 may use the following methods An operation is performed to obtain a corresponding one of the plurality of control coefficients Kp.

Kp _n =1+|[(ORP _target,n -ORP _1,n )]/ORP _target,n | Formula 7 Wherein, the ORP _target,n is the nth of the multiple ORP target values and the ORP _{1 , n} is the nth of the plurality of first ORP parameters. In this exemplary embodiment, the corresponding control parameter Kp obtained by formula 7 will be greater than or equal to 1.

In at least one example embodiment, when one of the plurality of first comparison results shows that a corresponding one of the plurality of first ORP parameters is greater than or equal to a corresponding one of the plurality of ORP target values, and the plurality of second ORP parameters When the corresponding one of the comparison results shows that the corresponding one of the plurality of second ORP parameters is greater than the corresponding first ORP parameter, the first control device 12 sets the corresponding one of the plurality of control coefficients Kp equal to 1.

In at least one example embodiment, when one of the plurality of first comparison results shows that a corresponding one of the plurality of first ORP parameters is smaller than a corresponding one of the plurality of ORP target values, and the plurality of second comparison results When the corresponding one of the plurality of second ORP parameters shows that the corresponding one of the plurality of second ORP parameters is less than or equal to the corresponding first ORP parameter, the first control device 12 sets the corresponding one of the plurality of control coefficients Kp equal to 1.

In at least one example embodiment, when one of the plurality of first comparison results shows that a corresponding one of the plurality of first ORP parameters is smaller than a corresponding one of the plurality of ORP target values, and the plurality of second comparison results When the corresponding one of the plurality of second ORP parameters shows that the corresponding one of the plurality of second ORP parameters is greater than the corresponding first ORP parameter, the first control device 12 may perform the operation in the following manner to obtain the corresponding one of the plurality of control coefficients Kp One.

Kp _n =1-|[ORP _target,n -ORP _1,n )]/ORP _target,n | Formula 8 Wherein, the ORP _target,n is the nth of the multiple ORP target values and the ORP _{1, n is} the nth of the plurality of first ORP parameters. In this exemplary embodiment, the corresponding control parameter Kp obtained by formula 8 will be less than 1.

For example, when the first ORP parameter generated by the ORP sensing device 11112 in the hypoxia pool 11022 is greater than or equal to the first ORP target value corresponding to the hypoxia pool 11022, and the ORP sensing device When the second ORP parameter generated by 11112 is greater than the first ORP parameter generated by the ORP sensing device 11112, the first control device 12 sets the control coefficient Kp of the hypoxia pool 11022 to be equal to 1.

At block S1250, the first control device 12 generates a plurality of control parameters according to the plurality of control parameters, and generates the plurality of operation parameters according to the plurality of control parameters.

In at least one example embodiment, the first control device 12 can generate a control base Kb through the plurality of control coefficients Kp, so as to adjust the plurality of control coefficients to generate a plurality of adjusted control coefficients Kp '. For example, the plurality of adjusted control coefficients Kp' can be generated by the following formula.

Kp' _n =Kp _n /Kb Formula 9 wherein, the number of the plurality of regulation coefficients is equal to k, and the Kp' _{n is} the nth of the plurality of adjusted regulation coefficients Kp'. In this exemplary embodiment, the regulation base Kb may be the sum or the average of the plurality of regulation coefficients Kp.

In at least one example embodiment, the first control device 12 can obtain the respective initial values of the plurality of gate valves 11461-11463 through the plurality of operating relationships and the initial frequency of the plurality of gate valves 11461-11463 flow. The plurality of initial flows can be generated by using the plurality of initial frequencies and the operating relationship, and by formula 10 described below.

Q _SF,n =a _SF,n ×[(OP _SF,n )^b _SF,n ] Formula 10 Among them, Q _SF,n is the flow rate of the nth gate valve, and OP _SF,n is the nth gate valve The opening of the orifice and a _SF,n and b _SF,n are the fixed parameters of the operating relationship of the nth gate valve.

In at least one example embodiment, the first control device 12 generates the respective adjusted flow rates according to the product of the respective initial flow rates of the plurality of gate valves 11461-11463 and the respective adjusted control coefficients Kp', that is, the adjustment is known. Subsequent return flow. In at least one example embodiment, the first control device 12 calculates and adjusts a plurality of orifice openings according to the plurality of segmented return flows and the plurality of fixed parameters, as the operation of the plurality of gate valves 11461-11463 parameter.

FIG. 13 shows a flow chart of the biological nitrification/denitrification program control process in the parameter monitoring and water resources treatment method of the exemplary implementation method of the present invention. Because of the variety of ways in which the exemplary method can be performed, the method is provided by way of example only. The methods described below may be performed using, for example, the systems and apparatus shown in FIGS. 1 and 2, and exemplary methods are explained with reference to various elements of these figures. Each block shown in FIG. 13 represents one or more routines, methods, or subroutines performed in the exemplary method. Furthermore, the order of the blocks is exemplary only and may vary. Additional blocks may be added or fewer blocks may be used without departing from the invention.

At block S1310, a plurality of sensing parameters sensed at a plurality of sensing moments from a sensing device 11 are received.

In at least one example embodiment, the sensing device 11 can periodically sense the value of a sensing variable of the water body 10 to be tested, and can also continuously sense the value of the sensing variable of the water body 10 to be tested.

In at least one example embodiment, the sensing device 11 may be an ORP sensing device, the sensing variable is an ORP variable, and the sensing parameters are the ORP sensing device at the sensing moments Sensed multiple ORP parameters. In at least one example embodiment, the sensing device 11 can be a pH sensing device, the sensing variable is a pH variable, and the sensing parameters are the pH sensing device at the sensing moments Multiple pH parameters sensed.

In at least one example embodiment, the first control device 12 receives the plurality of sensing parameters sensed by the sensing device 11 at the plurality of sensing moments. In this example embodiment, the second control device 13 is also receiving the sensing parameters indirectly provided by the first control device 12 or directly provided by the sensing device 11 . In this exemplary embodiment, the plurality of sensing moments may be consecutive or discontinuous. In this exemplary embodiment, the sensing device 11 may have a control period Δt in the time interval between each acquisition of the value of the sensing variable. Therefore, the interval between the plurality of sensing times may be m×Δt. In this example embodiment, m may be a positive integer.

In at least one example embodiment, the plurality of sensing instants may be five consecutive sensing instants t ₀ +(n-4)×Δt, t ₀ +(n-3)×Δt, with an interval of 1. t ₀ +(n-2)×Δt, t ₀ +(n-1)×Δt and t ₀ +n×Δt may also be four consecutive sensing times with an interval of 2. In another example embodiment, the plurality of sensing instants may be discontinuous four sensing instants t ₀ +(n-5)×Δt,t ₀ +(n-4)×Δt,t ₀ + (n-2)×Δt and t ₀ +n×Δt.

At block S1320, a prediction parameter is generated under a prediction model according to the plurality of sensed parameters.

In at least one example embodiment, the second control device 12 may generate the predicted parameter of a subsequent time under the prediction model according to the plurality of sensing parameters and the plurality of sensing times. In this example embodiment, when the last time of the plurality of sensing moments is t ₀ +n×Δt, the subsequent moment may be t ₀ +(n+1)×Δt.

In at least one example embodiment, the second control device 12 has a preset training module, the second control device 12 can receive a historical data including a large number of sensing time and sensing data in advance, and through the Historical data establishes the forecasting model in advance. In an exemplary embodiment, the prediction model can be modeled for various artificial intelligence architectures such as Artificial Neural Network (ANN) architecture, and the ANN mode is optimized by curves under the ANN architecture. to predict the prediction parameters. In at least one example embodiment, the second control device 12 is not limited to modeling in an ANN architecture, and can generate different prediction models through other machine learning methods. In this example embodiment, the prediction model may be an embodiment of the control decision model of the present invention.

At block S1330, at least one first operation value is calculated according to the plurality of sensing parameters, and at least one second operation value is calculated according to the plurality of sensing parameters and the prediction parameter.

In at least one example embodiment, the at least one first operation value may include one or two first differential values of the sensing variable at the sensing time t ₀ +n×Δt. The one or two first differential values may be a first-order differential value dP _t0+n×Δt and a second-order differential value d ² P _t0+n×Δt . Therefore, the second control device 12 obtains the at least one first operation value according to the plurality of sensing parameters.

In at least one example embodiment, the at least one second operation value may include one or two second differential values of the sensed variable at the predicted time instant t ₀ +(n+1)×Δt. The one or two second differential values may be the first-order differential value dP _{t0+(n+1)×Δt} and the second-order differential value d ² P _{t0+(n+1)×Δt} . Therefore, the second control device 12 obtains the at least one second operation value according to the predicted parameter and the plurality of sensing parameters.

At block S1340, a control parameter is determined according to at least one of the at least one first operation value and the at least one second operation value.

In at least one example embodiment, the control parameter is a biological nitrification/denitrification reaction endpoint. FIG. 14 shows the changes of the ORP variable and the pH variable of the water body 10 to be measured under the biological nitrification/denitrification reaction during the water resource treatment process of the water body 10 to be measured. It can be seen from Figure 14 that at the end of the biological nitrification/denitrification reaction, both the ORP variable and the pH variable have clear turning points ORP(B), pH(B), ORP(C) and pH(C), so it can be Through the generated prediction parameters, it is predicted whether the values of the ORP variable and the pH variable meet the turning point as the end point of the biological nitrification/denitrification reaction.

In at least one example embodiment, the second control device 12 can pass the first-order differential value dP _t0+n×Δt , the second-order differential value d ² P _t0+n×Δt , the first-order differential value dP _{t0+ (n+1)×Δt} and at least one of the second-order differential value d ² P _{t0+(n+1)×Δt} to evaluate whether the ORP variable and the pH variable meet as the end point of the biological nitrification/denitrification reaction the turning point. In this example embodiment, the slopes at multiple turning points ORP(B), ORP(C), pH(B), and pH(C) in FIG. reduce. Therefore, the second control device 12 can make a judgment through the comparison between the first-order differential value dP _t0+n×Δt and dP _{t0+(n+1)×Δt} . In this exemplary embodiment, the second control device 12 can also make a judgment through the comparison between the second-order differential value d ² P _t0+n×Δt and d ² P _{t0+(n+1)×Δt} . For example, the second control device 12 can pass the ratio of the division between the first-order differential value dP _t0+n×Δt and dP _{t0+(n+1)×Δt} , if an absolute value of the ratio is greater than When a preset threshold is set, it can be determined that the ORP variable and the pH variable have reached the turning point, which is the end point of the biological nitrification/denitrification reaction. Therefore, the ORP variable and the pH variable at the turning point can be set as the biological nitrification. / The reference point for the end point of the denitration reaction. In addition, the biological nitrification/denitrification program control process can also be used as the ORP target value judgment method in the parameter monitoring and water resources treatment method shown in Figure 3. Therefore, the ORP parameter of this turning point can be used as the ORP target value.

FIG. 15 is a flow chart showing the process of monitoring the biological activity index in the parameter monitoring and water resources treatment method of the exemplary implementation method of the present invention. Because of the variety of ways in which the exemplary method can be performed, the method is provided by way of example only. The methods described below may be performed using, for example, the systems and apparatus shown in FIGS. 1 and 2, and exemplary methods are explained with reference to various elements of these figures. Each block shown in FIG. 15 represents one or more routines, methods, or subroutines performed in the exemplary method. Furthermore, the order of the blocks is exemplary only and may vary. Additional blocks may be added or fewer blocks may be used without departing from the invention.

At block S1510, the first control device 12 receives a first monitoring parameter of a water body to be measured at a first sensing moment, and receives a sensing device for the water body to be detected at the first sensing moment from a sensing device A measured first sensing parameter.

In at least one example embodiment, the sensing device 11 can periodically sense the value of a sensing variable of the water body 10 to be tested, and can also continuously sense the value of the sensing variable of the water body 10 to be tested. In this exemplary embodiment, the sensing device 11 may be a DO sensing device, the sensing variable is a DO variable, and the first sensing parameter is sensed by the DO sensing device at the first sensing moment The first DO parameter measured.

In at least one example embodiment, the second control device 13 is also receiving the first sensing parameter indirectly provided by the first control device 12 or directly provided by the sensing device 11 . In this exemplary embodiment, the sensing device 11 may have a control period Δt in the time interval between each acquisition of the value of the sensing variable. Therefore, the interval between the plurality of sensing times may be m×Δt. In this example embodiment, m may be a positive integer.

In at least one example embodiment, the first monitoring parameter is the value of the aeration volume variable of the water body 10 to be measured at the first sensing time (ie, the first aeration volume parameter). In this exemplary embodiment, the first monitoring parameter can be calculated by other methods, and can also be obtained by a previously stored calculated value. For example, it can be read from the storage 222 .

In at least one example embodiment, the first control device 12 obtains a DO conversion rate parameter and other parameters such as the effective volume of an aerobic cell.

At block S1520, the first control device 12 receives a second sensing parameter sensed by the sensing device for the water body to be measured at a second sensing moment.

In at least one example embodiment, the first control device 12 may receive the second DO parameter sensed by the DO sensing device for the water body to be measured at the second sensing moment. In this example embodiment, the second sensing time is later than the first sensing time.

At block S1530, a Bio-Activity Indicator (BAI) of the water body to be measured is calculated according to the monitoring parameter, the first sensing parameter and the second sensing parameter.

In at least one example embodiment, the first control device 12 can calculate the biological activity index of the water body to be measured according to the first aeration amount parameter, the first DO parameter and the second DO parameter. In this example embodiment, the biological activity indicator BAI can be calculated as follows.

BAI _{r0+(n+1)×△t} =a×Q _t0+n×△t +[(DO _t0+n×△t -DO _{t0+(n+1)×△t} )]/△t| Equation 11 Wherein, BAI _{t0+(n+1)×Δt} and DO _{t0+(n+1)×Δt} are the values of the BAI parameter and the DO variable at the second sensing time, respectively, Q _t0+n×Δt and DO _t0+n×Δt are the value of the aeration quantity parameter and the DO variable at the first sensing time, respectively, and a is the DO conversion rate.

At block S1540, the first control device 12 calculates a second monitoring parameter of the water body to be measured at the second sensing time according to the biological activity index.

In at least one example embodiment, the first control device 12 can calculate an adjusted aeration amount of the water body to be measured according to the biological activity index and the DO conversion rate. In this example embodiment, the adjusted aeration amount is the second monitoring parameter at the second sensing time, and the second monitoring parameter can be calculated in the following manner.

Q _{t0+(n+1)×△t} =BAI _{t0+(n+1)×△t} /a Formula 12

Please refer to FIG. 16 . FIG. 16 is a schematic diagram of a sludge settling monitoring system 160 of the water resource monitoring and treatment system 1 according to an exemplary embodiment of the present invention. The sludge settling monitoring system 160 includes a first control device 12 , a plurality of sensing devices 1611 - 1612 , a sampling pump 1653 and a return valve 1654 . In this exemplary embodiment, the sludge settling monitoring system 160 samples from an aerobic pool through the sampling pump 1653, and adds the obtained water body to be tested into a test pool 1605 for automatic settling test. The plurality of sensing devices 1611-1612 can be the first MLSS sensing device 1611 and the second MLSS sensing device 1612, respectively, and measure the water body to be measured, respectively to obtain a plurality of first MLSS parameters and a plurality of second MLSS parameters. In the example embodiment, the first control device 12 generates the plurality of first control parameters through the first operation mode using the plurality of obtained first MLSS parameters and the plurality of second MLSS parameters. In this example embodiment, the heights of the first MLSS sensing device 1611 and the second MLSS sensing device 1612 are different in the test pool 1605 to sense the value of the MLSS variable at different water levels in the test pool 1605 .

In at least one example embodiment, the sludge settling monitoring system 160 further has the second control device 13 shown in FIG. 1 , and the second control device 13 also uses the acquired sensing parameters to The second operation mode generates a plurality of second control parameters, and the plurality of second control parameters can be used to replace the plurality of first control parameters.

In at least one example embodiment, after the water body to be tested is tested or settled in the test pool 1605 , the water body to be tested can be played back into the aerobic pool through the return valve 1654 .

Fig. 17 shows a flow chart of the process of monitoring the sludge settleability in the parameter monitoring and water resources treatment method of the exemplary implementation method of the present invention. Because of the variety of ways in which the exemplary method can be performed, the method is provided by way of example only. The methods described below may be performed using, for example, the systems and apparatus shown in FIGS. 1, 2, and 16, and exemplary methods are explained with reference to various elements of these figures. Each block shown in FIG. 17 represents one or more routines, methods, or subroutines performed in the exemplary method. Furthermore, the order of the blocks is exemplary only and may vary. Additional blocks may be added or fewer blocks may be used without departing from the invention.

At block S1710, the first control device 12 receives a plurality of MLSS parameters sensed by a first MLSS sensing device for a water body to be measured at a plurality of sensing times.

In at least one example embodiment, the first MLSS sensing device 1611 and the second MLSS sensing device 1612 can periodically sense the plurality of first MLSS parameters and the The plurality of second MLSS parameters can also continuously sense the value of the MLSS variable of the water body to be measured. In this exemplary embodiment, the first control device 12 may have a control period Δt in the time interval between each time obtaining the value of the MLSS variable.

At block S1720, the first control device 12 obtains the differential value of the plurality of first MLSS parameters at the first sensing moment among the plurality of sensing moments.

In at least one example embodiment, the first control device 12 may select the first sensing time among the plurality of sensing times to calculate the first sensing time of the first sensing time through the plurality of first MLSS parameters Differential value. In at least one example embodiment, the first control device 12 can directly receive the calculation result of the differential value from other devices.

In at least one example embodiment, the first sensing moment may be the last sensing moment among the plurality of sensing moments. In another example embodiment, the first sensing moment may retrieve any intermediate sensing moment from the plurality of sensing moments.

At block S1730, the first control device 12 confirms whether the differential value is less than zero. When the differential value is less than 0, the method will proceed to block S1740. When the differential value is greater than or equal to 0, the method will proceed to block S1750.

Fig. 18 shows the sedimentation changes of the MLSS of the water to be measured at different depths under the process of monitoring sludge sedimentation during the water treatment process of the water to be measured. In the sludge settling process, the time period from t ₀ to t _f belongs to the retardation settling period 1810 , the time period from t _f to t _u belongs to the transition settling period 1820 , and the time period after t _u belongs to 1830 during compaction settling. It can be seen from FIG. 18 that the slope values of sludge heights in different sedimentation periods are not the same, and the difference in slope between the retardation sedimentation period 1810 and the transition sedimentation period 1820 is particularly obvious. In at least one example embodiment, the first MLSS sensing device 1611 can be preset at the sludge height during the blocking sedimentation period 1810, in order to accurately calculate a sludge sedimentation velocity. In at least one example embodiment, the first MLSS sensing device 1611 can be set at a sludge height when switching from the retarded settling period 1810 to the switching settling period 1820 .

In at least one example embodiment, the first control device 12 confirms whether the differential value is less than zero. If the differential value is greater than 0, it means that the first MLSS parameter of the first MLSS sensing device 1611 at the sensing moment still belongs to the settling retardation period 1810, and the sludge settling velocity can be calculated. If the differential value is less than 0, it means that the first MLSS parameter of the first MLSS sensing device 1611 at the sensing moment does not belong to the settling block period 1810, and the sludge settling velocity cannot be calculated. In addition, if the differential value is less than 0, it may also mean that the position of the first MLSS sensing device 1611 is too high. Although the sensing moment still belongs to the settling block period 1810, the sludge height is already lower than the The position of the first sensing device 1611 cannot be used to calculate the sludge settling velocity.

At block S1740, the first control device 12 adjusts the first sensing moment.

In at least one example embodiment, the first control device 12 may reselect the first sensing time in order to find a specific sensing time. In this example embodiment, the specific sensing time still belongs to the blocking sedimentation period 1810, and the position of the first MLSS sensing device 1611 is still lower than the sludge height at the specific sensing time.

At block S1750, the first control device 12 calculates one monitoring parameter of the water body to be measured according to at least two MLSS parameters of the plurality of MLSS parameters.

In at least one example embodiment, the monitored parameter is the sludge settling velocity. In this example embodiment, the first control device 12 may obtain at least two first MLSS parameters from the plurality of first MLSS parameters to calculate the sludge settling velocity. In this example embodiment, at least two sensing times corresponding to the at least two first MLSS parameters are earlier than the first sensing time, so as to ensure that the block still belongs to the at least two sensing times. During the settling period 1810, the position of the first MLSS sensing device 1611 is still lower than the sludge height at the at least two sensing moments. In this exemplary embodiment, the at least two sensing moments can be an initial sensing time t _i and the first sensing time t _i +n×Δt, then the sludge settling velocity (Zone settling velocity, ZSV ) can be obtained by the following formula.

ZSV=(H _ti -H _ti+n×Δt )/[t _i -(t _i +n×Δt)] Formula 13 Wherein, H _ti is the initial sludge height at the initial sensing time t _i , And H _ti+n×Δt is a first sludge height at the first sensing time t _i +n×Δt.

In at least one example embodiment, the first control device 12 may additionally calculate a sludge terminal concentration and a sludge return rate. According to FIG. 18 , after the water body to be tested enters the transition and settlement period 1820 , the rising amplitudes of the plurality of second MLSS parameters sensed by the second MLSS sensing device have slowed down significantly. After the dense settling period 1830, the plurality of second MLSS parameters sensed by the second MLSS sensing device may even tend to be constant. Therefore, if the plurality of sensing moments have finally entered the transition sedimentation period 1820, the first control device 12 can find a first turning point of the sludge sedimentation height from the plurality of sensing moments to obtain a first turning point of the sludge sedimentation height. a turning point time t _f . In this example embodiment, the first control device 12 may further calculate the plurality of second MLSS parameters according to the plurality of second MLSS parameters between the first turning time tf and a last sensing time _Tf among the plurality of sensing times The sludge terminal concentration Xu. In an exemplary embodiment, the first control device 12 may average all the second MLSS parameters between the first turning time tf and the last sensing time _Tf to calculate the sludge terminal concentration Xu.

In at least one example embodiment, the first control device 12 can obtain in advance an MLSS concentration value Xa of the water body to be measured in the aerobic pool to be sampled, and use the calculated sludge terminal concentration Xu, to calculate the sludge return rate r. The sludge return rate r can be obtained by the following formula.

r=Xa/(Xu-Xa) Formula Fourteen

Since the effectiveness factors affecting the biological activated sludge process and the biological nitrogen and phosphorus removal process include the amount of microorganisms, the time of biochemical reaction, the activity of microorganisms, the supply of carbon sources and the number of electron acceptors, these factors are different in program control. The corresponding parameters are MLSS, SRT, HRT, OUR or SOUR, ZSV, C/N, C/P and DO. Besides MLSS and DO can be measured directly by the instrument, other parameters require a lot of time to measure. Experimentation is gained and immediacy is lost. Therefore, through the parameter monitoring water resource processing method and module of the present invention, the water resource monitoring and processing system 1 can be based on parameters such as MLSS, DO, pH, ORP, etc. that can be directly measured by the instrument, and can be further estimated by MLSS and DO The obtained ZSV and BAI parameters are used to obtain sludge return flow, biological nitrification/denitrification reaction end point, aeration volume, segmented inflow flow, internal return flow of nitrification solution and DO, and use these instantly obtained control parameters to Regulates each controlled device.

Figure 19 shows a schematic diagram of a water resource monitoring and treatment system according to an example embodiment of the present invention. The water to be tested enters an anaerobic tank 1901 , an anoxic tank 1902 , an aerobic tank 1903 and a sedimentation tank 1904 one by one from the direction of sewage inflow. In this example embodiment, a blower 1941 is used to maintain the aeration of the aerobic tank 1903 at an air volume. In this example embodiment, an internal return pump 1943 is used in the aerobic tank 1903 to control the internal return flow of a nitrifying solution between the aerobic tank 1903 and the anoxic tank 1902. In this example embodiment, a sludge return pump 1944 is provided in the settling tank 1904 to control an internal return flow of sludge between the settling tank 1904 and the anaerobic tank 1901 .

In at least one example embodiment, the water body to be tested is detected by a plurality of sensing devices 1911 in the anaerobic tank 1901, and the plurality of sensing devices 1911 can be divided into a first MLSS sensing device, a first an ORP sensing device and a first pH sensing device. The water body to be tested is detected by a plurality of sensing devices 1912 in the anoxic tank 1902, and the plurality of sensing devices 1912 can be divided into a second MLSS sensing device, a second ORP sensing device and a first Two pH sensing devices. the test The water body is detected by a plurality of sensing devices 1913 in the aerobic pool 1903, and the plurality of sensing devices 1913 can be divided into a third MLSS sensing device, a third ORP sensing device, and a third pH sensing device measuring device and a DO sensing device.

In at least one example embodiment, the nitrifying solution internal reflux control system 6 of the present invention can be used in the anoxic tank 1902 to obtain a plurality of second sensing data of the plurality of sensing devices 1912, and thereby calculate the The internal reflux volume of the nitrification solution is supplied to the internal reflux pump 1943.

In at least one example embodiment, a biological activity index monitoring device 1960 can calculate the aerobic pool 1903 through the plurality of third sensing data of the plurality of sensing devices 1913 through the biological activity index monitoring process of the present invention A BAI and an adjusted aeration amount of the water body to be measured are then used for the adjustment through the real-time aeration control system 4 of the present invention, and also through the plurality of third sensing data of the plurality of sensing devices 1913 The post-aeration volume is further adjusted to control the air volume of the blower 1941 driven into the aerobic tank 1903 .

In at least one example embodiment, a sludge settling monitoring system 160 can calculate a sludge of the water body to be measured in the aerobic tank 1903 by using the third sensing data of the sensing devices 1913 Sludge terminal concentration, through the sludge return control system 7 of the present invention, and also through the plurality of third sensing data of the plurality of sensing devices 1913, the sludge return flow rate is further confirmed for the sludge terminal concentration , to control the flow rate of a sludge return of the sludge return pump 1944.

In at least one example embodiment, the first control device 12 shown in FIG. 1 receives the plurality of first sensing parameters, the plurality of second sensing parameters from the plurality of sensing devices 1911 , 1912 and 1913 parameters and the plurality of third sensing parameters, and pass through the nitrifying liquid internal reflux control system 6, the biological activity index monitoring device 1960, the real-time aeration control system 4, the sludge sedimentation monitoring system 160 and the sludge A backflow control system 7 is used to adjust the amount of aeration, the internal return flow of the nitrifying solution and the Sludge return flow.

In at least one example embodiment, the first control device 12 shown in FIG. 1 further provides the plurality of first sensing parameters, the plurality of second sensing parameters and the plurality of third sensing parameters to The second control device 13 . The second control device 13 can establish different prediction models for each of the anaerobic tank 1901 , the anoxic tank 1902 and the aerobic tank 1903 .

In at least one example embodiment, the second control device 13 may establish an anaerobic prediction model for the anaerobic tank 1901 according to historical data provided by the plurality of sensing devices 1911 in the past. The second control device 13 uses the anaerobic prediction model to generate a predicted sludge through the first MLSS parameters, the first ORP parameters and the first pH parameters sensed in the anaerobic tank 1901 return flow. The second control device 13 can provide the predicted sludge return flow to the first control device 12, so that the first control device 12 can adjust the sludge return pump 1944 according to the predicted sludge return flow. In this example embodiment, the anaerobic prediction model is used to predict the sludge return rate, so the anaerobic prediction model may be a sludge return rate inference model. In this example embodiment, the sludge return rate inference model can be a specific embodiment of the control decision model of the present invention.

In at least one example embodiment, the second control device 13 can establish a hypoxia prediction model for the hypoxia pool 1902 according to historical data provided by the plurality of sensing devices 1912 in the past. The second control device 13 uses the hypoxia prediction model to generate a predicted nitrification solution through the second MLSS parameters, the second ORP parameters and the second pH parameters sensed in the anoxic tank 1902 Internal return flow. The second control device 13 can provide the predicted nitrification solution internal return flow to the first control device 12, so that the first control device 12 can adjust the internal reflux pump 1943 according to the predicted nitrification solution internal return flow. In this example embodiment, the hypoxia prediction model is used to predict the internal return flow of the nitrifying solution, so the hypoxia prediction model may be an inference model for the internal return flow of the nitrification solution. In this exemplary embodiment, the inference model of the internal reflux amount of the nitrification solution can be a specific embodiment of the control decision model of the present invention.

In at least one example embodiment, the second control device 13 can establish an aerobic prediction model for the aerobic pool 1903 according to historical data provided by the plurality of sensing devices 1913 in the past. The second control device 13 uses the aerobic prediction model to generate a predicted aeration through the third MLSS parameters, the third ORP parameters and the third pH parameters sensed in the aerobic tank 1903 quantity. The second control device 13 can provide the predicted aeration amount to the first control device 12, so that the first control device 12 adjusts the blower 1941 according to the predicted aeration amount. In this example embodiment, the aerobic prediction model is used to predict the aeration rate, so the aerobic prediction model may be an aeration rate inference model. In this exemplary embodiment, the aeration rate inference model may be a specific embodiment of the control decision model of the present invention.

In at least one example embodiment, the second control device 13 may establish a first water resource prediction model according to historical data provided by the plurality of sensing devices 1911-1913 in the past. The second control device 13 uses the first water resource prediction model to generate the predicted sludge return flow and the predicted aeration amount at one time through the first sensing parameters and the third sensing parameters. . The second control device 13 can provide the predicted sludge return flow and the predicted aeration amount to the first control device 12, so that the first control device 12 can simultaneously adjust the blower 1941 according to the predicted aeration amount , and adjust the sludge return pump 1944 according to the predicted sludge return flow. In this exemplary embodiment, the first water resource prediction model is used to predict the sludge return flow and the aeration amount at the same time, so the first water resource prediction model may be an anaerobic aerobic (AO) Program-controlled inference models. In this example embodiment, the AO program control inference model may be an embodiment of the effectiveness evaluation model of the present invention.

In at least one example embodiment, the second control device 13 may establish a second water resource prediction model according to historical data provided by the plurality of sensing devices 1911-1913 in the past. The second control device 13 uses the second water resource prediction model to generate the predicted pollution at one time by using the plurality of first sensing parameters, the plurality of second sensing parameters and the plurality of third sensing parameters mud The return flow, the predicted internal return flow of the nitrification solution, and the predicted aeration amount. The second control device 13 can provide the predicted sludge return flow, the predicted internal nitrification liquid return flow, and the predicted aeration amount to the first control device 12, so that the first control device 12 can simultaneously rely on the prediction The blower 1941 is adjusted according to the aeration amount, the internal return pump 1943 is adjusted according to the predicted nitrification liquid return flow, and the sludge return pump 1944 is adjusted according to the predicted sludge return flow. In this example embodiment, the second water resource prediction model is used to simultaneously predict the sludge return flow, the internal return flow of the nitrification solution and the aeration amount, so the second water resource prediction model may be an anaerobic deficiency Anaerobic Anoxic Oxide (A2O) program-controlled inference model. In this example embodiment, the A2O process control inference model can be an embodiment of the effectiveness evaluation model of the present invention.

In at least one example embodiment, the second control device 13 may establish a water quality prediction model according to historical data provided by the plurality of sensing devices 1911-1913 in the past. The second control device 13 uses the water quality prediction model to generate a plurality of water quality prediction parameters by using the plurality of first sensing parameters, the plurality of second sensing parameters and the plurality of third sensing parameters. In this exemplary embodiment, the plurality of water quality prediction parameters may include a predicted chemical oxygen demand (Chemical Oxygen Demand, COD), a predicted total nitrogen (Total Nitrogen, TN), a predicted ammonia nitrogen (NH ₃ -N), etc. Parameters of water quality indicators. The second control device 13 can use the plurality of water quality prediction parameters to know whether the final water quality of the water body to be measured can meet the requirements of each pool under the operation of the plurality of operating parameters of the existing controlled devices 14 . If the predicted water quality parameter has exceeded the standard, the second control device 13 itself can re-adjust the plurality of operating parameters of each controlled device 14, or the second control device 13 can notify the first control device 11 to re-adjust the various operating parameters. The plurality of operating parameters of the controlled device 14 . If the predicted water quality parameter does not exceed the standard, the plurality of operating parameters of each controlled device 14 can be maintained. In this exemplary embodiment, the water quality prediction model may be an embodiment of the effectiveness evaluation model of the present invention.

In at least one example embodiment, the second control device 13 can establish a control effect prediction model according to historical prediction data provided in the past, such as the predicted sludge return flow, the predicted nitrification liquid internal return flow, and the predicted aeration amount in the past. . The second control device 13 uses the control effect prediction model to evaluate the performance of each water quality parameter through the predicted sludge return flow, the predicted nitrification liquid internal return flow and the predicted aeration amount, so as to evaluate the compliance of each water quality parameter probability and the probability of exceeding the standard. If the probability of exceeding the standard is significantly high, even if the plurality of water quality prediction parameters have not exceeded the standard, the second control device 13 can still readjust the plurality of operating parameters of each controlled device 14, or the second control device 13 itself The control device 13 notifies the first control device 11 to readjust the plurality of operating parameters of the respective controlled devices 14 . In this exemplary embodiment, the control effectiveness prediction model may be a specific implementation of the effectiveness evaluation model of the present invention.

In at least one example embodiment, the second control device 12 has a preset training module, the second control device 12 can receive in advance the historical data including a large number of sensing times and sensing data, and through the These historical data are established in advance in the anaerobic prediction model, the anoxic prediction model, the aerobic prediction model, the first water resource prediction model, the second water resource prediction model, the control effect prediction model and the water quality prediction model. at least one. In an example embodiment, the anaerobic prediction model, the anoxic prediction model, the aerobic prediction model, the first water resource prediction model, the second water resource prediction model, the control effectiveness prediction model and the water quality prediction The model can be modeled by various artificial intelligence architectures such as an Artificial Neural Network (ANN) architecture, and the prediction parameters can be predicted through a curve-optimized ANN mode under the ANN architecture. In at least one example embodiment, the second control device 12 is not limited to modeling in an ANN architecture, and can generate different prediction models through other machine learning methods.

In at least one example embodiment, the artificial intelligence modeling architecture may include ANNs, single-layer neural networks, FNNs, feedback neural networks, self-organizing neural networks, structural adaptive neural networks, adversarial neural networks, and random neural network. In this example embodiment, the FNN can Including single-layer FNN, multi-layer FNN and linear neural network. In this example embodiment, the single-layer FNN may comprise a single-layer perceptron. In this example embodiment, the multi-layer FNN may include an RBF neural network, a BP neural network, a fully connected neural network, and a CNN. In this example embodiment, the RBF neural network may include a GRNN as well as a PNN. In this example embodiment, the linear neural network may comprise a Madline neural network. In this example embodiment, the feedback neural network may include RNN, BSB, and Hopfield neural network. In this example embodiment, the RNN may comprise an Elman neural network. In this example embodiment, the Hopfield neural network may include a discrete Hopfield neural network as well as a continuous Hopfield neural network. In this example embodiment, the self-organizing neural network may include a competitive neural network, an ART neural network, and a SOM neural network. In this example embodiment, the structure-adaptive neural network may comprise a Cascade-Correlation Network. In this example embodiment, the stochastic neural network may comprise a Boltzmann Machine.

Through the first control device 12 and the second control device 13 described in this case, in the process of the water resource monitoring and treatment method, by establishing different AI inference models to associate different biological activity parameters with control parameters, the water resources can be optimized. Adaptability of the resource monitoring and processing system 1 . At the same time, the water resource monitoring and treatment system 1 can quickly adjust the operating parameters of the controlled device when the parameters change, so as to ensure the best water resource treatment efficiency.

As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand: The technical solutions described in the embodiments are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

To sum up, the present invention complies with the requirements of an invention patent, and a patent application can be filed in accordance with the law. However, the above descriptions are only the preferred embodiments of the present invention, for those who are familiar with the art of the present case, Equivalent modifications or changes made in accordance with the creative spirit of this case shall be included within the scope of the following patent applications.

S310-S350: Steps

Claims (12)

An electronic device for water treatment, the electronic device comprising: at least one processor; and a storage device coupled to the at least one processor and storing a plurality of instructions, the plurality of instructions being processed by the at least one processor When executed by one processor, the at least one processor: obtains at least one target value of oxidation-reduction potential (ORP); receives a first sensed value of a water body to be measured from the ORP sensing device at a first sensing moment ORP parameter; comparing the first ORP parameter with the at least one ORP target value to generate a first comparison result; generating a control parameter according to the first comparison result; and regulating a process for processing the water body to be measured according to the control parameter An operating parameter of a controlled device, wherein: the at least one ORP target value includes a first ORP target value and a second ORP target value, and the first comparison result is that the first ORP parameter is greater than or equal to the first ORP The target value, the first ORP parameter is less than the first ORP target value and greater than or equal to the second ORP target value or the first ORP parameter is less than the second ORP target value. The electronic device of claim 1, wherein the plurality of instructions, when executed by the at least one processor, further cause the at least one processor to: when the first ORP parameter is less than the first ORP target value and greater than or equal to When the second ORP target value is used, confirm whether a second ORP parameter sensed by the ORP sensing device on the water body to be measured is less than or equal to the first ORP parameter at a second sensing time; When the ORP parameter is greater than the first ORP parameter, a regulation coefficient greater than 1 is generated according to the first ORP target value; When the second ORP parameter is less than or equal to the first ORP parameter, the control coefficient is set equal to 1; and the control parameter is generated according to an initial parameter of the controlled device and the control coefficient. The electronic device of claim 1, wherein the plurality of instructions, when executed by the at least one processor, further cause the at least one processor to: when the first ORP parameter is greater than or equal to the first ORP target value or the When the first ORP parameter is smaller than the second ORP target value, the control parameter is generated according to a parameter extreme value of the controlled device. The electronic device according to claim 1, wherein the controlled device is a flow pusher disposed in the water body to be tested, and when the first ORP parameter is greater than or equal to the first ORP target value, the flow pusher is is activated and an operating frequency of the streamer is set to a preset frequency. The electronic device of claim 1, wherein the plurality of instructions, when executed by the at least one processor, further cause the at least one processor to: obtain at least one parameter target range; A sensing parameter sensed on the water body to be measured at the sensing time; when the sensing parameter exceeds the target range of the parameter, the operation parameter of the controlled device is adjusted according to a parameter extreme value of the controlled device; and when the sensing parameter is within the parameter target range, comparing the first ORP parameter with the at least one ORP target value to generate a first comparison result. The electronic device of claim 5, wherein: when the parameter sensing device is a dissolved oxygen (DO) sensing device, the at least one parameter target range and the first sensing parameter are at least one DO target range and DO parameters, and When the parameter sensing device is a mixed liquid suspended solids (MLSS) sensing device, the at least one parameter target range and the first sensing parameter are at least one MLSS target range and an MLSS parameter, respectively. A method for water resource treatment of an electronic device, comprising: obtaining at least one oxidation-reduction potential (ORP) target value; a first ORP parameter; comparing the first ORP parameter with the at least one ORP target value to generate a first comparison result; generating a control parameter according to the first comparison result; and regulating and processing the water body to be measured according to the control parameter An operating parameter of a controlled device, wherein: the at least one ORP target value includes a first ORP target value and a second ORP target value, and the first comparison result is that the first ORP parameter is greater than or equal to the first ORP target value An ORP target value, the first ORP parameter is less than the first ORP target value and greater than or equal to the second ORP target value or the first ORP parameter is less than the second ORP target value. The method of claim 7, further comprising: when the first ORP parameter is less than the first ORP target value and greater than or equal to the second ORP target value, confirming that the ORP sensing device has a second ORP parameter. Whether a second ORP parameter sensed by the water body to be measured at the sensing time is less than or equal to the first ORP parameter; when the second ORP parameter is greater than the first ORP parameter, the first ORP target value is generated according to the first ORP target value greater than or equal to the first ORP parameter A control coefficient of 1; when the second ORP parameter is less than or equal to the first ORP parameter, set the control coefficient equal to 1; and The control parameter is generated according to an initial parameter of the controlled device and the control coefficient. The method of claim 7, further comprising: when the first ORP parameter is greater than or equal to the first ORP target value or the first ORP parameter is less than the second ORP target value, according to the controlled device's a parameter extrema to generate the control parameter. The method of claim 7, wherein the controlled device is a flow pusher disposed in the water body to be tested, and when the first ORP parameter is greater than or equal to the first ORP target value, the flow pusher is activated And an operating frequency of the flow pusher is set as a preset frequency. The method according to claim 7, further comprising: obtaining at least one parameter target range; receiving a sensing parameter sensed by the parameter sensing device on the water body to be measured at the first sensing moment; The sensing parameter is out of the parameter target range, adjusting the operating parameter of the controlled device according to a parameter extreme value of the controlled device; and when the sensing parameter is within the parameter target range, the first ORP parameter A comparison with the at least one ORP target value produces a first comparison result. The method of claim 11, wherein when the parameter sensing device is a dissolved oxygen (DO) sensing device, the at least one parameter target range and the first sensing parameter are at least one DO target range and DO, respectively parameter, and when the parameter sensing device is a mixed liquid suspended solids (MLSS) sensing device, the at least one parameter target range and the first sensing parameter are at least one MLSS target range and an MLSS parameter, respectively.

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