CN118092196B

CN118092196B - Water ecological restoration method and system capable of conducting layered adjustment of water body

Info

Publication number: CN118092196B
Application number: CN202410511337.2A
Authority: CN
Inventors: 陈伟昌; 张伟; 韦三刚; 周小壮; 安雪; 魏小婷; 张兵; 陈高峰; 赵帅; 赵博文; 张彬培; 杨跃
Original assignee: Shenzhen Shenshui Water Resources Consulting Co ltd; Pearl River Hydraulic Research Institute of PRWRC
Current assignee: Shenzhen Shenshui Water Resources Consulting Co ltd; Pearl River Hydraulic Research Institute of PRWRC
Priority date: 2024-04-26
Filing date: 2024-04-26
Publication date: 2024-06-28
Anticipated expiration: 2044-04-26
Also published as: CN118092196A

Abstract

The invention relates to the technical field of water ecological restoration, in particular to a water ecological restoration method and system capable of conducting layered regulation of a water body. The method comprises the following steps: acquiring target water body restoration area data; performing self-adaptive hydrological parameter acquisition according to the target water body restoration area data to generate corrected hydrological sampling data; constructing a target hydrodynamic model based on the target water body restoration area data; carrying out water body restoration layer division processing on the corrected hydrologic sampling data by utilizing the target hydrodynamic model to generate water body restoration layer data; carrying out water lifting repair operation according to the water repair layer data to generate water repair operation data; constructing a fuzzy logic control model based on a preset fuzzy matching rule base; and transmitting the water body repair operation data to a fuzzy logic control model to perform optimal control strategy fuzzy reasoning, and performing device operation optimization adjustment to obtain an intelligent closed-loop control strategy. The invention can realize water ecological restoration through intelligent water layered regulation.

Description

Water ecological restoration method and system capable of conducting layered adjustment of water body

Technical Field

The invention relates to the technical field of water ecological restoration, in particular to a water ecological restoration method and system capable of conducting layered regulation of a water body.

Background

One of the main reasons for forming black and odorous water bodies is that the water bodies are anoxic, and the phenomenon is further aggravated by layering of the water bodies, so that anaerobic organisms in anoxic water bodies, particularly deep water bodies, are greatly propagated, and severe water changes are caused. However, the traditional water ecological restoration method can only realize the transverse circulation of the surface water body, cannot improve the vertical layering phenomenon of the water body, and cannot radically solve the water body eutrophication problems such as algal bloom burst and the like; meanwhile, the existing longitudinal water pumping circulation scheme can only pump water at a fixed depth, and cannot effectively treat water bodies of different water layers.

Disclosure of Invention

Based on the above, the invention provides a water ecological restoration method and a water ecological restoration system capable of carrying out layered regulation on a water body, so as to solve at least one of the technical problems.

In order to achieve the above purpose, the water ecological restoration method capable of carrying out layered regulation on the water body comprises the following steps:

step S1: acquiring target water body restoration area data; performing self-adaptive hydrological parameter acquisition according to the target water body restoration area data to generate self-adaptive hydrological sampling data; performing outlier correction processing on the self-adaptive hydrologic sampling data to generate corrected hydrologic sampling data;

Step S2: performing hydrodynamic modeling according to the target water body restoration area data to generate a target hydrodynamic model; performing dissolved oxygen transmission simulation on the corrected hydrologic sampling data by using a target hydrodynamic model to generate dissolved oxygen transmission simulation data; carrying out water body repair layer division processing according to the dissolved oxygen transmission simulation data to generate water body repair layer data;

Step S3: performing target restoration data transmission on the controller through the terminal equipment based on the water restoration layer data to obtain target water restoration data; carrying out water body lifting restoration operation according to the target water body restoration data to generate water body restoration operation data; transmitting the water body restoration operation data to terminal equipment, and performing device feedback control so as to obtain monitoring adjustment data;

Step S4: extracting key operation parameters according to the water body restoration operation data to respectively obtain feedback quantity parameters and control quantity parameters; constructing a fuzzy logic control model through feedback quantity parameters and control quantity parameters based on a preset fuzzy matching rule base; transmitting the water body restoration operation data to a fuzzy logic control model to perform optimal control strategy fuzzy reasoning, and generating optimal fuzzy reasoning control data; and carrying out device operation optimization adjustment on the monitoring adjustment data through the optimal fuzzy inference control data, thereby obtaining an intelligent closed-loop control strategy.

According to the invention, by acquiring the detailed data of the target water body restoration area, including the information of topography, water quality, water flow and the like, the characteristics of the restoration area can be comprehensively known, and in the process of self-adaptive hydrological parameter acquisition according to the data of the target water body restoration area, the system can acquire and adjust the dynamic hydrological parameters according to the real-time data and the characteristics of the area, so that the self-adaption can ensure that the acquired hydrological parameters are more accurate and reliable. The process of carrying out abnormal value correction processing on the self-adaptive hydrologic sampling data is beneficial to eliminating abnormal or error values in the sampling data, and the reliability and accuracy of the data are ensured. And establishing a water body fluid dynamic model by using the acquired data, and taking the factors such as terrain, water flow speed, river bed structure and the like into consideration. The model may reveal water flow characteristics including eddies, turbulence, and the like. And carrying out dissolved oxygen transmission simulation on the corrected hydrologic sampling data by using the target hydrodynamic model. The method is beneficial to predicting the distribution condition of oxygen in the water body, providing key information to evaluate the oxygen saturation of the water body, and carrying out division treatment of the water body repair layer based on dissolved oxygen transmission simulation data. By dividing the water body layers with different depths, the vertical structure of the water body is determined, and the method is beneficial to identifying potential problem areas and repair areas with priority. The terminal equipment is used for transmitting the water body repair layer data to the controller, so that the immediate target repair data transmission is realized, the instantaneity and the accuracy in the repair process are ensured, and the controller can make an accurate repair decision based on the latest data. Based on the received target water body restoration data, the controller performs planning and execution of water body lifting restoration operation, ensures that the restoration operation performs accurate operation according to actual needs, and adjusts the water body structure and water quality distribution to the greatest extent, so as to achieve the target restoration effect. And the water body repair operation data is transmitted back to the terminal equipment, so that related personnel can monitor the execution condition of the repair operation in real time, the visibility and the transparency of the repair process are improved, and a decision maker is helped to better understand and evaluate the repair effect. And extracting key operation parameters from the water body repair operation data, wherein the key operation parameters comprise operation time, operation area, repair layer depth and the like. This is useful for a deep understanding of key features of the actual repair operation. And constructing a fuzzy logic control model by using the extracted parameters. The model can consider multiple factors including water state, environmental conditions and the like, can also process the ambiguity of input parameters, and realizes intelligent response to complex environmental changes. The water body repair operation data are transmitted to the fuzzy logic control model, and the system can adjust the control strategy in real time to adapt to the changed repair demands, so that the optimal repair effect is achieved, the adaptability and the optimality of the repair effect are improved, and the system is ensured to flexibly cope with different operation environments. Through real-time monitoring and intelligent adjustment, the system can continuously optimize the repair operation, so that a more continuous and excellent repair effect is obtained. the water ecological restoration method capable of carrying out layered regulation on the water body, disclosed by the invention, utilizes the lifting motor to collect the dissolved oxygen content of different layers of the water body, utilizes the micro-lifting diversion technology to form strong water body three-dimensional circulation in the water body, promotes the exchange of shallow and deep water bodies, improves the dissolved oxygen content of the water body, especially the deep water body, and adopts a closed-loop control strategy to optimally control the rotating speed and the time length of the water pumping motor, improves the control efficiency, ensures that the dissolved oxygen content of the deep water body reaches the standard under the conditions of low cost and low energy consumption, can accurately restore the black and odorous water body, thoroughly avoids the blackness and the odor of the treated water body, and is a powerful measure for preventing, controlling and maintaining the black and odorous water body.

Preferably, step S1 comprises the steps of:

step S11: acquiring target water body restoration area data;

step S12: performing water depth prediction according to the target water restoration area data to generate water depth prediction quantity data;

step S13: setting depth gradient sampling points according to the water depth measurement data, and carrying out depth uniform distribution treatment so as to obtain depth measurement point data;

Step S14: based on the depth measurement point data, the controller is used for controlling the lifting motor to collect the self-adaptive hydrological parameters, and self-adaptive hydrological sampling data are generated;

step S15: and carrying out outlier correction processing on the self-adaptive hydrologic sampling data to generate corrected hydrologic sampling data.

According to the method, the depth prediction is carried out on the target water body restoration area data, the system can acquire the depth information of the water body at different positions, and the prediction provides the spatial structure of the water body. The system obtains a series of depth measurement point data by setting the depth gradient sampling points and uniformly distributing the depth of the water body depth measurement data. These depth measurement point data are beneficial to achieving full coverage of the body of water, ensuring that the acquired hydrologic parameters are representative. Through evenly distributed depth measurement points, the system can more comprehensively and evenly know the vertical structure of the water body. By utilizing depth measurement point data, the system implements adaptive hydrological parameter acquisition. By controlling the motion of the lift motor, the system can acquire hydrological parameter data at different depths. By performing outlier correction processing on the adaptive hydrologic sampling data, the system obtains more reliable and accurate corrected hydrologic sampling data.

Preferably, step S14 comprises the steps of:

step S141: performing depth sampling strategy processing according to the depth measurement point data to generate depth sampling strategy data;

Step S142: based on depth sampling strategy data, a controller is used for controlling a lifting motor to sequentially stop measuring points for water body data acquisition, and water layer dissolved oxygen array data are acquired through a dissolved oxygen sensor array; collecting water layer flow direction data through a flow velocity and flow direction instrument; collecting water layer temperature data through a temperature sensor;

step S143: performing temperature depth sequence processing according to the water layer temperature data to generate water layer temperature sequence data;

Step S144: carrying out temperature differential calculation on the water layer temperature sequence data so as to obtain temperature change rate data; filtering and smoothing the temperature change rate data to generate temperature smooth change data;

Step S145: carrying out temperature layer junction detection on the temperature smooth change data through preset temperature layer junction threshold data to obtain temperature layer junction detection data;

Step S146: when the temperature layer junction detection data does not exist, acquiring water layer dissolved oxygen array data, water layer flow direction data and water layer temperature data based on depth sampling strategy data to integrate hydrologic data so as to obtain static hydrologic sampling data;

Step S147: when the temperature layer junction exists in the temperature layer junction detection data, carrying out dynamic sampling strategy adjustment on the depth sampling strategy data to generate dynamic sampling strategy data; and acquiring hydrologic data based on the dynamic sampling strategy data to obtain dynamic hydrologic sampling data.

According to the invention, by reasonably formulating the depth sampling strategy, the system can acquire representative hydrologic data at each depth point of the water body. According to the depth sampling strategy data, the system utilizes the controller to control the lifting motor, and water data acquisition is carried out in the process that the measuring points are sequentially stopped. This ordered acquisition of measurement points ensures that the system systematically acquires hydrologic data in the depth direction. By processing the water layer temperature data, the system generates water layer temperature sequence data. This sequence records the trend of the temperature of the water body with the depth. And the differential calculation is performed on the water layer temperature sequence data, so that noise interference is reduced, and trend information of temperature change is reserved. And the temperature stratification in the water body is indicated by detecting the temperature stratification of the temperature smooth change data. When the detection data show that the temperature layer junction does not exist in the water body, all sampling points are acquired according to the depth sampling strategy data. When the detection data show that the water body has a temperature layer junction, the system dynamically adjusts the depth sampling strategy, and hydrologic data acquisition is carried out based on dynamic sampling strategy data to obtain dynamic hydrologic sampling data.

Preferably, step S147 includes the steps of:

Step S1471: performing time slicing processing on the temperature layer junction detection data to generate depth time sequence slice data; dividing the detection water layer according to the depth time sequence slice data to respectively obtain a detected water layer region, a temperature layer junction detection region and an undetected water layer region;

step S1472: calculating the thickness of the layer junction according to the depth time sequence slice data, so as to obtain temperature layer junction thickness data;

Step S1473: performing trend fitting treatment on the temperature layer junction thickness data by using a preset linear regression model to generate layer junction trend fitting data;

step S1474: carrying out key change point identification according to the layer junction trend fitting data to generate key change point data;

Step S1475: sampling point self-adaptive density optimization is carried out on depth sampling strategy data through key change point data, and sampling depth optimization data is generated;

Step S1476: dynamic sampling strategy adjustment is carried out according to the sampling depth optimization data, and dynamic sampling strategy data are generated;

Step S1477: and controlling the lifting motor to acquire hydrologic data of the temperature layer junction detection area and the undetected water layer area by using the controller based on dynamic sampling strategy data, and carrying out data combination on the hydrologic data acquired by the detected water layer area to generate dynamic hydrologic sampling data, wherein the dynamic hydrologic sampling data comprises dynamic water layer dissolved oxygen array data, dynamic water layer flow direction data and dynamic water layer temperature data.

According to the invention, through time slicing, the system can capture the dynamic change process of the temperature layer junction. And according to the depth time sequence slice data, the system divides the water layer to obtain a detected water layer region, a temperature layer junction detection region and an undetected water layer region. Through the depth time sequence slice data, the system calculates the thickness of the layer junction, and is helpful for quantitatively describing the intensity and distribution condition of the temperature layer junction in the water body. And carrying out trend fitting treatment on the temperature layer junction thickness data by using a preset linear regression model, thereby being beneficial to analyzing the development trend of the temperature layer junction. And identifying key change points through the layer junction trend fitting data, and obtaining key change point data by the system. These change points mark the moments when the temperature layer junction changes significantly. The depth sampling strategy data is subjected to sampling point self-adaptive density optimization through the key change point data, so that hydrological data can be collected more densely at key moments, and the response to temperature layer junction change events is ensured to be more flexible and timely. Based on dynamic sampling strategy data, the system controls the lifting motor to acquire hydrologic data of the temperature layer junction detection area and the undetected water layer area through the controller, thereby being beneficial to acquiring more comprehensive and accurate hydrologic information.

Preferably, step S2 comprises the steps of:

Step S21: performing water type mining on the target water body restoration area data to generate target water body type data;

Step S22: performing hydrodynamic modeling according to the target water body type data to generate a target hydrodynamic model;

step S23: carrying out water flow direction simulation on the corrected hydrologic sampling data through a target hydrodynamic model to generate water flow direction simulation data; performing temperature distribution simulation on the corrected hydrologic sampling data through a target hydrodynamic model to generate temperature distribution data;

step S24: performing dissolved oxygen transmission simulation on the corrected hydrologic sampling data by utilizing a target hydrodynamic model based on the water flow direction simulation data and the temperature distribution data to generate dissolved oxygen transmission simulation data;

step S25: carrying out water layering characteristic extraction according to the dissolved oxygen transmission simulation data, the water flow direction simulation data and the temperature distribution data to generate water layer characteristic data;

step S26: performing deep-dissolved oxygen correlation processing according to the dissolved oxygen transmission simulation data to generate deep-dissolved oxygen correlation data;

step S27: and carrying out water body restoration layer division processing on the water layer characteristic data by utilizing the depth-dissolved oxygen associated data to generate water body restoration layer data.

The invention establishes a target hydrodynamic model based on the target water type data. The model reflects the movement rules of the fluid in the water body, including key characteristics such as flow speed, flow direction and the like, and is helpful for simulating the movement state of the fluid in the water body. And simulating the corrected hydrologic sampling data by using the target hydrodynamic model. The water flow direction simulation data reflects the flow direction of the water in different areas, and the temperature distribution simulation data describes the temperature change distribution in the water. The system performs dissolved oxygen transmission simulation through the target hydrodynamic model, which is helpful for understanding the distribution situation of the dissolved oxygen in the water body, and provides simulation prediction of the dissolved oxygen content. The system performs feature extraction of the water body layering, which is helpful for identifying feature layers with different depths in the water body. The system performs depth-dissolved oxygen correlation treatment, and establishes a relation between depth and dissolved oxygen content. This helps to understand the distribution of dissolved oxygen in the body of water at different depths. By depth-dissolved oxygen correlation data, the system performs water body restoration layer division processing on the water layer characteristic data, which is helpful for dividing the water body into different restoration layers.

Preferably, step S3 comprises the steps of:

step S31: performing target restoration data transmission on the controller through the terminal equipment based on the water restoration layer data to obtain target water restoration data;

step S32: performing optimal restoration depth calculation on the target water restoration data by using a lifting control algorithm to generate restoration layer depth distance data;

Step S33: the method comprises the steps that a lifting motor is controlled by a controller to move the repairing layer distance based on repairing layer depth distance data, a pumping operation task is executed by a pumping motor driving module, water flow is transmitted to a water body processing module through a diversion structure module to carry out water body repairing treatment, a strong transverse and longitudinal flow circulating water body is formed, repairing operation parameter collection is carried out by a telemetry terminal, and water body repairing operation data are generated, wherein the water body repairing operation data comprise device operation parameter data and real-time repairing layer hydrological parameters;

Step S34: transmitting the water body repair operation data to a terminal device through a Bluetooth transmission protocol to obtain monitoring feedback data;

step S35: and performing device feedback control through the controller based on the monitoring feedback data, thereby obtaining monitoring adjustment data.

According to the invention, the terminal equipment is used for transmitting the water body repair layer data to the controller, so that the effective transmission of the target water body repair data is realized, and the accuracy and real-time data used in the repair process are ensured. And the system processes the received target water body restoration data through a lifting control algorithm, calculates to obtain the optimal restoration depth, ensures that the water body restoration operation is carried out at the optimal depth, and improves the restoration effect. The water body restoration processing is carried out by controlling the restoration layer depth distance data to execute the water pumping operation task, and the real-time restoration layer hydrological parameter data collected by the telemetry terminal provides real-time data support for subsequent monitoring and adjustment. And the Bluetooth transmission protocol is used for transmitting the water body repair operation data to the terminal equipment, so that the timely acquisition of monitoring feedback data is realized. The monitoring feedback data contains the actual effect of the repair operation, hydrologic parameters and other information, and is helpful for monitoring the water repair process in real time. By analyzing the monitoring feedback data, the system can implement feedback control of the device, adjust the water body restoration operation, and ensure real-time adjustment and monitoring of the water body restoration process.

Preferably, step S4 comprises the steps of:

Step S41: extracting key operation parameters according to the water body restoration operation data to respectively obtain water layer dissolved oxygen content data, lifting motor descending depth data, water pumping motor rotating speed data, rotating time length data and device electric quantity data, wherein the water layer dissolved oxygen content data is marked as a feedback quantity parameter, and the lifting motor descending depth data, the water pumping motor rotating speed data, the rotating time length data and the device electric quantity data are marked as control quantity parameters;

Step S42: performing optimization index calculation on the feedback quantity parameters and the control quantity parameters by using a feedback control algorithm to generate feedback optimization index data;

step S43: carrying out fuzzy set division on the feedback quantity parameter and the control quantity parameter so as to obtain fuzzy set data of the monitoring variable;

Step S44: performing label naming processing on the fuzzy set data of the monitoring variable to obtain fuzzy set label data;

Step S45: performing membership processing on fuzzy set tag data through a preset fuzzy matching rule base to construct a fuzzy logic control model;

Step S46: performing feedback optimization judgment on feedback optimization index data through a preset feedback optimization threshold value, and performing device control adjustment through a controller based on monitoring adjustment data when the feedback optimization index data is lower than the feedback optimization threshold value; when the feedback optimization index data is higher than or equal to the feedback optimization threshold value, transmitting the water body repair operation data to a fuzzy logic control model to perform optimal control strategy fuzzy reasoning so as to generate optimal fuzzy reasoning control data;

step S47: performing fuzzy inversion processing according to the optimal fuzzy inference control data so as to obtain optimal control instruction data;

Step S48: and carrying out device operation optimization adjustment on the optimal control instruction data through the controller based on the monitoring adjustment data, thereby obtaining an intelligent closed-loop control strategy.

According to the invention, by extracting the key parameters of the water body restoration operation, the system can acquire important information in the operation process. The dissolved oxygen content is regarded as a feedback quantity parameter, parameters such as a lifting motor, a water pumping motor and the like are marked as a control quantity parameter, and optimization index calculation is carried out on the feedback quantity parameter and the control quantity parameter, so that the method is beneficial to evaluating the operation effect and the device state according to real-time water body restoration operation data. And the fuzzy set division is carried out on the feedback quantity parameters and the control quantity parameters, so that a more flexible and robust fuzzy logic control model is built. Fuzzy set data provides a fuzzy description of parameter variations that can better accommodate uncertainty and complexity. Membership degree processing is carried out on fuzzy set label data through a fuzzy matching rule base to form a fuzzy logic control model, and intelligent control on different input conditions is realized by establishing control rules of a system. According to the comparison of the feedback optimization index data and the preset threshold value, the system can judge whether the effect of the current water body restoration operation reaches the expected or not, and the intelligent decision mechanism is beneficial to realizing the self-adaptive adjustment of the water body restoration operation. And performing device operation optimization adjustment on the optimal control instruction data through the controller to form an intelligent closed-loop control strategy. The closed-loop control strategy realizes the intellectualization and self-adaption of the water body restoration operation through continuous optimization, and ensures the high-efficiency operation of the system in a complex environment.

The invention also provides a water ecological restoration system capable of carrying out water layered regulation, which executes the water ecological restoration method capable of carrying out water layered regulation, and comprises the following steps:

The self-adaptive hydrologic sampling module is used for acquiring target water body restoration area data; performing self-adaptive hydrological parameter acquisition according to the target water body restoration area data to generate self-adaptive hydrological sampling data; performing outlier correction processing on the self-adaptive hydrologic sampling data to generate corrected hydrologic sampling data;

The repair layer identification module is used for carrying out hydrodynamic modeling according to the data of the target water body repair area to generate a target hydrodynamic model; performing dissolved oxygen transmission simulation on the corrected hydrologic sampling data by using a target hydrodynamic model to generate dissolved oxygen transmission simulation data; carrying out water body repair layer division processing according to the dissolved oxygen transmission simulation data to generate water body repair layer data;

The intelligent lifting control module is used for carrying out target restoration data transmission on the controller through the terminal equipment based on the water restoration layer data to obtain target water restoration data; carrying out water body lifting restoration operation according to the target water body restoration data to generate water body restoration operation data; transmitting the water body restoration operation data to terminal equipment, and performing device feedback control so as to obtain monitoring adjustment data;

The closed-loop control optimization module is used for extracting key operation parameters according to the water body restoration operation data to respectively obtain feedback quantity parameters and control quantity parameters; constructing a fuzzy logic control model through feedback quantity parameters and control quantity parameters based on a preset fuzzy matching rule base; transmitting the water body restoration operation data to a fuzzy logic control model to perform optimal control strategy fuzzy reasoning, and generating optimal fuzzy reasoning control data; and carrying out device operation optimization adjustment on the monitoring adjustment data through the optimal fuzzy inference control data, thereby obtaining an intelligent closed-loop control strategy.

Drawings

FIG. 1 is a schematic flow chart of the steps of the water ecological restoration method capable of carrying out layered regulation of a water body.

FIG. 2 is a detailed flowchart illustrating the implementation of step S1 in FIG. 1;

FIG. 3 is a flowchart illustrating the detailed implementation of step S3 in FIG. 1;

FIG. 4 is a schematic diagram of the feedback monitoring process in step S34 of FIG. 3;

FIG. 5 is a schematic diagram of the control thread logic flow of FIG. 4.

The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

In order to achieve the above objective, referring to fig. 1 to 5, the present invention provides a water ecological restoration method capable of performing layered adjustment of a water body, comprising the following steps:

In the embodiment of the present invention, as described with reference to fig. 1, the step flow diagram of a water ecological restoration method capable of performing water body layered regulation according to the present invention is provided, and in the embodiment, the water ecological restoration method capable of performing water body layered regulation includes the following steps:

In the embodiment of the invention, the system acquires accurate hydrologic information by defining the geographical range of the target water body restoration area and collecting the historical water quality monitoring station and groundwater data. And (3) carrying out depth measurement by using equipment such as a water depth sensor, cleaning abnormal values, processing missing data and generating evenly distributed depth measurement point data. And adjusting parameters of the lifting motor, and adaptively acquiring hydrologic parameters according to the depth measurement point data. And detecting and correcting abnormal values of the acquired data to ensure the data quality. And finally, obtaining corrected hydrologic sampling data, and providing a foundation for water body restoration.

In the embodiment of the invention, the water body type classification and the water characteristic mining are carried out through the target water body restoration area data, and the result is digitized into the target water body type data (such as lakes, rivers and wetlands). Modeling by adopting a Navier-Stokes equation, and generating a target hydrodynamic model by considering the fluid characteristics of different water body types. The model considers the conditions of relatively static lakes, relatively fast river currents and the like. And simulating the water flow direction and the temperature distribution by using the hydrodynamic model to generate corresponding data. And inputting the flow speed, flow direction and temperature information into a model, and performing dissolved oxygen transmission simulation to generate dissolved oxygen concentration data at each sampling point. And extracting water layering characteristics by combining dissolved oxygen transmission simulation, water flow direction simulation and temperature distribution simulation. Determining a water body restoration layer division criterion through the depth-dissolved oxygen associated data, and distributing the water body restoration layer information to each sampling point to form final water body restoration layer data.

In the embodiment of the invention, a terminal device (such as a smart phone and a tablet personal computer) and a controller are prepared, communication connection is established through Bluetooth, and transmission parameters are set to ensure smooth communication. And the controller selects and triggers the water body repair layer data to be transmitted to the terminal equipment, and the staff adjusts the parameters of the treatment equipment according to the data. The controller analyzes feedback information returned by the terminal equipment and generates a device adjusting instruction. And calculating the optimal repair depth by using a lifting control algorithm, and generating repair layer depth data. The calculated depth distance represents the distance the prosthetic device needs to be raised/lowered. The controller transmits the water body repair operation data to the terminal equipment through a Bluetooth transmission protocol, so that data transmission and two-way communication are realized. The controller transmits the device adjusting instruction to the execution unit of the water body repairing equipment through the internal signal transmission module, and meanwhile, hydrologic parameters of the repairing process are monitored, so that the real-time property and accuracy of device adjustment are ensured.

In the embodiment of the invention, the water body restoration equipment acquires water body restoration operation data, including the content of dissolved oxygen, the lifting depth, the pumping rotation speed, the rotation time, the electric quantity and the like. And using a feedback control algorithm, taking the dissolved oxygen content as a feedback quantity and other parameters as control quantities, and calculating to generate feedback optimization index data. Fuzzy logic control is adopted to carry out fuzzy division on parameters and establish a rule base. And calculating the membership degree of the control operation through the fuzzy rule, and comprehensively obtaining the optimal control instruction. And judging whether fuzzy reasoning is needed or not according to the feedback optimization threshold value. If the optimal control strategy is needed, transmitting data to a fuzzy logic control model, and carrying out optimal control strategy reasoning and inversion to obtain optimal control instruction data. Transmitting the instruction to a controller, adjusting parameters such as the depth of the lifting motor, the rotating speed of the water pumping motor and the like, and realizing intelligent closed-loop control of the water body restoration system.

Preferably, step S1 comprises the steps of:

step S11: acquiring target water body restoration area data;

As an example of the present invention, referring to fig. 2, a detailed implementation step flow diagram of step S1 in fig. 1 is shown, where step S1 includes:

step S11: acquiring target water body restoration area data;

in embodiments of the present invention, the area of the body of water to be repaired is determined, which may involve a particular river, lake or sea area. Ensuring the geographic extent of the defined area. And collecting the existing water quality monitoring station data in the target water body area to acquire historical water quality information. This relates to data provided by the health sector, the environmental protection agency or the research agency. If the remediation involves groundwater, groundwater level and water quality data for the target area is obtained, and local water resource management authorities may provide relevant data.

in the embodiment of the invention, actual water depth data is collected from hydrologic monitoring stations or water surveys in a target water restoration area. For example, depth measurement is performed in a target water restoration area by using a water depth sensor, sonar and other devices, so as to obtain actual water depth data.

In the embodiment of the invention, the depth data is cleaned, abnormal values are deleted, missing data is processed, and the accuracy of the data is ensured. For example, a depth outlier that may be caused by a device failure is deleted. And calculating the depth gradient and determining the setting of the sampling point. For example, in a region where the depth gradient is large, more dense sampling points are set. And uniformly distributing the sampling points in the region with smaller gradient. The distribution of depth measurement points in the whole water body restoration area is ensured to be relatively uniform. The interval between sampling points is set to control the density of sampling points. In the region with larger depth gradient, the interval can be reduced, and the density of sampling points can be increased. And generating depth measurement point data based on the calculated sampling point coordinates. Each measurement point includes geographic coordinates and a corresponding depth value. And the boundary effect of the water body restoration area is noted, so that the depth measurement points are properly distributed in the boundary area. It is contemplated that boundary effects may be handled by extrapolation or other methods. And generating final depth measurement point data according to the actual depth measurement data, gradient setting and uniform distribution processing.

In the embodiment of the invention, the operation of the lifting motor is adjusted according to the depth measurement point data. The parameter settings may include movement speed, dwell time, data sampling frequency, etc. And reading depth information of the sampling points from the depth measurement point data generated before, initializing the lifting motor to the surface of the water body, and preparing for next acquisition of hydrological parameters. The starting position of the lifting motor is ensured to be above the surface of the water body so as to avoid collision and interference. And the controller is used for controlling the operation of the lifting motor according to the depth measurement point data, so that the lifting motor can adaptively move along the vertical direction of the water body. For example, in areas with greater depth gradients, motor speed may be moderately faster, while in areas with lesser depth gradients, motor speed may be moderately slower. And controlling the lifting motor to move according to the next depth point in the depth measurement point data. And performing self-adaptive sampling control and hydrological parameter acquisition in a circulating way until the sampling of all depth points is completed. And stopping running after the lifting motor finishes sampling all depth points, and carrying out data caching on the acquired self-adaptive hydrologic sampling data.

In the embodiment of the invention, an abnormal value in the self-adaptive hydrologic sampling data is detected by using a statistical method or a model. For example, abnormal values are detected using a box map, or abnormal detection is performed using a machine learning algorithm. And correcting the detected abnormal value. The methods of correction may include deleting outliers, interpolating with neighboring values, using mean or median substitutions, etc. And verifying the corrected hydrologic sampling data to ensure that the abnormal value is effectively corrected. The outlier detection may be performed again, the quality of the corrected data may be confirmed, and the corrected hydrographic sample data may be saved as final corrected hydrographic sample data.

Preferably, step S14 comprises the steps of:

In the embodiment of the invention, depth sampling strategy parameters including sampling interval, depth resolution and the like are set according to actual requirements and research purposes. For example, depth points for which water data acquisition is required are generated near each sampling point at intervals of 0.5 meters. And determining depth points required to acquire the water body data according to the depth measurement point data. For example, the sampling points are stagnant at each sampling point according to depth. The water body data acquisition equipment such as the dissolved oxygen sensor array, the flow velocity and flow direction instrument, the temperature sensor and the like are ensured to be in a normal working state, and calibration is carried out according to the requirement. The controller is used for controlling the lifting motor to sequentially stop at depth points appointed in the depth sampling strategy data. The controller adjusts the motor motion according to the depth information to ensure dwell at each depth point. And when the motor is stopped at the appointed depth point, starting the dissolved oxygen sensor array to acquire data. Dissolved oxygen array data was recorded for each depth point. And starting the flow velocity and direction instrument to collect water layer flow velocity and direction data. Flow rate and direction data for each depth point is recorded. And starting a temperature sensor to acquire water layer temperature data. The water layer temperature data for each depth point was recorded. And according to the sequence of the depth sampling points, finishing the temperature data of the water layer to form temperature depth sequence data. Ensuring that the data is ordered from shallow to deep in depth. And carrying out differential calculation on the water layer temperature sequence data to obtain temperature change rate data. For example, the temperature differential of adjacent depth points is calculated. And filtering and smoothing the temperature change rate data to reduce noise and abrupt change. Methods such as moving average, gaussian filtering, etc. may be used. And setting a threshold value of the temperature layer junction according to the research requirement and the priori knowledge. For example, a set temperature change rate exceeding 0.2 degrees celsius/m is a temperature layer junction. And detecting the temperature smooth change data according to the set threshold value, and identifying whether a temperature layer junction exists or not. And when the temperature layer junction detection data does not exist, sampling by utilizing depth sampling strategy data to obtain dissolved oxygen array data, water layer flow direction data and water layer temperature data of corresponding depth points. And integrating the acquired hydrologic data into static hydrologic sampling data. Ensuring that the data is ordered from shallow to deep. And dynamically adjusting a depth sampling strategy according to the temperature layer junction detection data, and increasing or reducing sampling points at the temperature layer junction. For example, the density of sampling points is increased at the temperature layer junction. And acquiring hydrologic data by using dynamic sampling strategy data, wherein the hydrologic data comprises dissolved oxygen array data, water layer flow direction data and water layer temperature data. It is ensured that sampling points are added at the temperature layer junction to capture the water body characteristics more finely. And integrating the acquired hydrologic data into dynamic hydrologic sampling data. Ensuring that the data is ordered from shallow to deep in depth.

Preferably, step S147 includes the steps of:

In the embodiment of the invention, the temperature layer junction detection data is subjected to time slicing processing, and is divided into a plurality of time sequence slices according to time sequence. And carrying out water layer division on the temperature layer junction detection data of each time slice to obtain a detected water layer region, a temperature layer junction detection region and an undetected water layer region. And calculating the thickness of the layer junction according to the depth data of each time sequence slice, namely the depth range of the temperature layer junction detection area. A linear regression model is preset for fitting the trend of the temperature layer junction thickness data. And carrying out trend fitting treatment on the temperature layer junction thickness data by using a preset linear regression model to obtain layer junction trend fitting data. And calculating the change rate of the layer junction trend fitting data, finding out the time point when the change rate exceeds a preset threshold value, and identifying the time point as a key change point. In the temperature layer junction fitting data, if the trend value at a certain moment rises from 10 to 15 and the change rate exceeds a set threshold value, the moment is identified as a key change point. The key change point data includes a time point and a corresponding trend value. And adjusting a depth sampling strategy according to the key change point data, and increasing or decreasing the sampling point density near the key change point. For example, a critical change point at a time, which indicates that a significant change in the body of water has occurred, may be added with more sampling points around the time to increase the density of data samples. The optimized depth sample data reflects adjustments near key change points. And dynamically adjusting the depth sampling strategy according to the optimized sampling depth data, and generating a final dynamic sampling strategy. For example, sampling depth optimization data at a time reflects a higher sampling point density, and depth sampling strategies may be adjusted based on this density to ensure more detailed acquisition of hydrographic data at that time. According to a dynamic sampling strategy, a controller is used for controlling a lifting motor to acquire hydrological data of a temperature layer junction detection area and an undetected water layer area. For example, the lift motor is sequentially lowered to a specified depth according to a dynamic sampling strategy, and data acquisition is performed on each depth sampling point for a certain time by using the dissolved oxygen sensor array. After the collection is completed, the lifting motor is lifted to the next depth, and the steps are repeated until the collection of the whole water body is completed. And integrating the dynamic hydrological data acquired by the detected water layer area with the hydrological data of the undetected water layer area.

Preferably, step S2 comprises the steps of:

In the embodiment of the invention, the water body type classification is carried out according to the target water body restoration area data, and the specific water characteristic data of the water body is mined. And digitizing the result of the water body type mining to generate target water body type data. For example, a lake is denoted by numeral 1, a river is denoted by numeral 2, and a wetland is denoted by numeral 3. Based on the water body type data, modeling is performed by adopting a Navier-Stokes equation. Parameters of the fluid dynamics model are adjusted in consideration of different fluid characteristics of lakes, rivers and wetlands. And inputting the water body type data into the fluid dynamic model to generate a target fluid dynamic model. For example, lake regions may be considered more static, river regions may be considered faster water flow, etc. And inputting the flow velocity and flow direction information in the corrected hydrologic sampling data into a model by using the target hydrodynamic model, and performing water flow direction simulation. For example, consider the direction of water flow in a river region and consider relatively static in a lake region. And generating water body flow direction simulation data according to the simulation result, wherein the data comprise flow direction, flow speed and other information of each sampling point. And inputting temperature information in the corrected hydrologic sampling data into a model by using the target hydrodynamic model, and performing temperature distribution simulation. Consider, for example, the effects of sunlight, water flow, and temperature. According to the result of the temperature distribution simulation, temperature distribution data including a temperature value of each sampling point is generated. And inputting the water flow direction simulation data and the temperature distribution data into a target hydrodynamic model, and simulating the transmission process of the dissolved oxygen in the water. The influence of factors such as flow rate, temperature and the like on the concentration of dissolved oxygen is considered. Based on the simulation results, dissolved oxygen concentration data at each sampling point is generated. For example, a dissolved oxygen concentration value is obtained for each time point for each location. And carrying out layered characteristic extraction on the water body by utilizing a specific algorithm or method by combining the dissolved oxygen transmission simulation data, the water body flow direction simulation data and the temperature distribution data. For example, the stratification characteristics of a body of water are determined by analyzing the distribution of dissolved oxygen at different depths and temperatures. The depth and dissolved oxygen concentration of each sampling point are correlated. Statistical analysis or other algorithms may be employed to determine the correlation between depth and dissolved oxygen. And generating correlation data between the depth and the dissolved oxygen concentration according to the result of the correlation processing. For example, a relationship curve or table between depth and dissolved oxygen concentration is obtained. And (5) based on the depth-dissolved oxygen related data, establishing a criterion for dividing the water body repair layer. For example, the body of water is divided into different remediation layers according to a particular threshold of dissolved oxygen concentration. And dividing the water layer characteristic data into different water body restoration layers according to the division criteria. And generating final water body repair layer data, wherein the final water body repair layer data comprises repair layer information of each sampling point.

Preferably, step S3 comprises the steps of:

As an example of the present invention, referring to fig. 3, a detailed implementation step flow diagram of step S3 in fig. 1 is shown, where step S3 includes:

In the embodiment of the invention, terminal equipment for data transmission, such as a smart phone, a tablet computer and the like, and controller equipment are prepared. A corresponding communication application is installed on the terminal device or a wireless communication protocol is used to establish a communication connection with the controller. This may be achieved by bluetooth wireless transmission. Data transmission parameters such as transmission protocol, data format, etc. are set on the terminal device. Ensuring that communication between the terminal device and the controller is smooth. And selecting target water body repair layer data through the controller, and triggering a data transmission command. And the terminal equipment encapsulates the water body repair layer data into a transmission format and transmits the transmission format to the terminal equipment through the established communication connection. The staff adjusts the working parameters of the water body treatment equipment according to the repair layer information through the terminal equipment, or makes a specific water body repair plan, and feeds the water body repair plan back to the controller, the controller receives feedback information transmitted by the terminal equipment, and analyzes the received data according to a predefined data format, so that target water body repair data are obtained.

In the embodiment of the invention, the initial parameters of the lifting control algorithm are set according to the characteristics of the water body restoration data. This may include initial depth, lifting speed, range of variation of the depth of the repair layer, etc. And inputting the target water body restoration data into a lifting control algorithm, and starting the algorithm to calculate the optimal restoration depth. And after the lifting control algorithm calculates the optimal repair depth, generating repair layer depth data. The depth represents the depth position at which the water remediation device should operate in the water to achieve optimal remediation. The lifting control algorithm can efficiently realize the calculation of the optimal repair depth, and can realize the lifting depth processing by other conventional technologies, but cannot calculate the optimal depth. And comparing the calculated depth of the repair layer with the actual depth of each sampling point, and calculating the depth distance. This may be the depth of the prosthetic layer minus the absolute value of the actual depth, indicating the distance the prosthetic device needs to be raised or lowered. For example, in a lake restoration project, the elevation control algorithm takes into account the distribution of dissolved oxygen, the direction of water flow and the change in water depth in different areas of the lake bottom. After the initial parameters are set, the algorithm finds the optimal restoration depth through restoration layer depth calculation, and the generated restoration layer depth distance data is displayed in the center of the lake and needs to be vertically lowered by 5 meters, and the lake shore needs to be vertically lowered by 3 meters, so that the optimal restoration effect is realized.

In the embodiment of the invention, the controller moves the telescopic guide pipe and the sensor device to the target depth position through the lifting motor according to the repair layer depth distance data. The controller executes a pumping operation task through the pumping motor driving module, the diversion structure part comprises a diversion pipe, a water diversion disk and a water pressing disk, the diversion pipe is a level hose and is telescopic in length, and the bottom end of the diversion pipe extends into the lower part of the deep water layer and is provided with a water inlet; the water distribution plate is connected with the other end of the guide pipe and is fixedly connected with the buoy platform through the bracket; the water pressing disc is fixed below the buoy platform and is positioned above the impeller; a transverse diversion space for water flow is formed between the water distributing disc and the water pressing disc. When the water pumping motor rotates, the water at the fixed layer enters the water distribution disc under the action of the impeller, under the combined action of the water distribution disc and the water pressing disc, the water body is diffused outside the device in a micro-lifting state and forms transverse flow, local negative pressure is formed near the impeller at the moment, and under the action of atmospheric pressure, the deep water body enters the guide pipe from the water inlet hole at the bottom of the pipe and is lifted to the shallow layer, so that transverse flow circulation and longitudinal flow circulation are formed. And acquiring the repair operation parameters in real time by using a telemetry terminal. This may include elevation depth, pumping rate, water quality parameters, etc. And the controller generates water body restoration operation data according to the data acquired by the telemetry terminal. Including device operational parameter data (e.g., elevation depth, pumping rate, etc.) and real-time repair layer hydrologic parameters (e.g., dissolved oxygen concentration, temperature, etc.).

In the embodiment of the invention, the controller sorts the water body restoration operation data according to the real-time data collected in the water body restoration operation process, wherein the water body restoration operation data comprises device operation parameter data and real-time restoration layer hydrologic parameters. And a Bluetooth communication module is configured on the water body restoration equipment, so that the water body restoration equipment is compatible with the controller and supports a Bluetooth transmission protocol. The controller encodes and packages the water body repair operation data through the internal processing module so as to facilitate Bluetooth transmission. The controller starts the Bluetooth module and establishes Bluetooth connection with the terminal equipment. This may be a pre-paired bluetooth connection or a connection may be established dynamically when required. The controller transmits the water body repair operation data to the terminal equipment through a Bluetooth transmission protocol. In the data transmission process, the Bluetooth protocol is adopted to carry out stable data transmission, so that the integrity and accuracy of the data are ensured. And the terminal equipment monitors and analyzes the received water body restoration operation data to generate monitoring feedback data. For example, the quality of the body of water is assessed from the dissolved oxygen concentration data. The terminal equipment transmits the monitoring feedback data back to the controller of the water body restoration equipment through a Bluetooth transmission protocol to form two-way communication.

In the embodiment of the invention, the controller receives the monitoring feedback data transmitted by the terminal equipment, and the analysis data comprises various monitoring indexes such as the concentration of dissolved oxygen, the water temperature and the like. Based on the analysis of the monitoring feedback data, the controller generates device adjustment instructions. This instruction contains specific regulatory requirements for each device in the body of water remediation apparatus. The controller transmits the device adjustment instructions to each execution unit inside the water body restoration apparatus through an internal signal transmission module, such as an electrical signal or a control bus. And when the device is adjusted, the controller continuously monitors the hydrological parameters in the repairing process in real time, so that the real-time property and accuracy of the device adjustment are ensured.

Preferably, the formula of the lift control algorithm in step S32 is as follows:

；

In the method, in the process of the invention, Expressed as the optimal repair layer depth distance,Represented as a mathematical argument symbol,Represented as a repair depth variable,Represented as a minimum repair depth value,Represented as a maximum repair depth value,Denoted as the wavelength of the healing layer,Represented as a phase value of the repair layer,Represented by a natural constant which is a function of the natural constant,Expressed as a value of the circumference,Represented as the attenuation factor of the repair layer,Represented as a repair layer density parameter,Represented as a lift control influence value,Represented as water body remediation layer state data,Represented as pre-repair status data for the water body repair layer,Expressed as a repair layer stability factor.

The invention utilizes a lifting control algorithm which fully considers the restoration depth variableMinimum repair depth valueMaximum repair depth valueWavelength of repair layerPhase value of repair layerCircumference valueAttenuation factor of repair layerDensity parameter of repair layerValue of influence of elevation controlLayer data of water body restorationPre-repair status data of water body repair layerStability coefficient of repair layerAnd interactions between functions to form a functional relationship:

That is to say, By taking into account the minimum repair depth valueMaximum repair depth valueAverage calculation of repair depth, i.eAs an initial repair layer, a repair depth variable is representedOr a center point of the center point. This center point may represent an expected optimal value or a steady state reference point. In the optimization problem, it can be used to determine the center of the search space, so that the best solution is explored around this center point in the iterative process. Sub-itemsPeriodic fluctuations in repair layer depth over time were simulated. Wherein,The period of the waveform, i.e. the frequency of the depth change of the repair layer, is controlled; the starting position of the waveform, i.e. the phase at which the repair starts, is determined. The periodic variation of the depth of the repair layer is reflected, and the attenuation of the repair effect with increasing depth is also considered. I.e. the effect of the healing layer is strongest near the surface of the body of water, since at this point The attenuation effect is not obvious due to smaller size. With depthThe repair effect is reduced by the exponential decay. By adjustingAndThe periodic variation of the healing effect may be controlled to accommodate for the periodic factors in the environment. By adjustingThe decay rate of the healing effect with depth can be controlled. Sub-itemsIn (a)Representing the change in density in the repair layer.AndIs logarithmized to adjust the depth of repairEffect on repair effect, especially whenIs nonlinear.Is an adjusting parameter, which is matched withThe number of pairs of numbers is taken after multiplication,And the water layer influence value caused by the lifting depth of the repairing layer. Sub-itemsRepresenting the intensity of the reparative effect, whereinIs the current state data of the repair layer,Is pre-repair status data, andIs the stability factor of the repair layer. The geometrical average of these parameters is taken into account by the calculation, thus balancing the effect of the different parameters on the final result. Thus, the optimal repair depth is calculated by comprehensively considering the periodic variation, the attenuation rate, the density variation and the current state of the repair layer.

Preferably, step S4 comprises the steps of:

In the embodiment of the invention, water body restoration operation data are acquired from a sensor and a recording device of the water body restoration device. The assumed data includes water layer dissolved oxygen content, lifting motor descending depth, water pumping motor rotating speed, rotating time length, device electric quantity and the like. And marking the data of the dissolved oxygen content of the water layer as feedback quantity parameters, and marking the data of the descending depth of the lifting motor, the rotating speed of the water pumping motor, the rotating time and the electric quantity of the device as control quantity parameters. And calculating an optimization index by using a designed feedback control algorithm, wherein the content of dissolved oxygen in a water layer is used as a feedback quantity, and the descending depth of a lifting motor, the rotating speed of a water pumping motor, the rotating time length and the electric quantity of the device are used as control quantities. And generating feedback optimization index data according to the calculated optimization index. This may include ideal values of the feedback quantity parameters, adjustment suggestions of the control quantity parameters, etc. The precondition of fuzzy set division of feedback quantity parameters (such as water layer dissolved oxygen content) and control quantity parameters (such as lifting motor descending depth, pumping motor rotating speed and the like) is to determine the dividing range and the dividing quantity. For example, the dissolved oxygen content data of the water layer is divided according to a set fuzzy set division rule, so that corresponding fuzzy set data are obtained. For example, the dissolved oxygen content is divided into three fuzzy sets of "low", "medium", "high". And dividing control quantity parameters such as the descending depth of the lifting motor, the rotating speed of the water pumping motor and the like according to a set fuzzy set dividing rule to obtain corresponding fuzzy set data. For example, the depth is divided into three fuzzy sets of "shallow", "medium" and "deep", and the rotation speed is divided into three fuzzy sets of "low", "medium" and "high". And (3) naming the labels of each fuzzy set in the fuzzy set data so as to construct a subsequent fuzzy logic control model. For example, "low", "medium", "high" may be used as labels for dissolved oxygen content, "shallow", "medium", "deep" may be used as labels for depth, and "low", "medium", "high" may be used as labels for rotational speed. And (3) sorting the fuzzy set data with the labels into label data, wherein each data point comprises a corresponding feedback quantity label and a control quantity label. For example, a piece of data may include a "medium" dissolved oxygen content and a "deep" depth. A set of fuzzy rules is preset to prescribe membership assignment under different label combinations. For example, "if the dissolved oxygen is low and the depth is shallow," the low-speed rotation is performed. And applying a fuzzy rule base to the tag data, and calculating the membership degree of each rule. For example, if the data is "medium" dissolved oxygen content and "deep" depth, the membership degree for performing "medium speed rotation" is calculated from the rule base. And integrating the membership degrees calculated by all the rules to obtain the membership degrees for executing different control operations. For example, the "medium speed rotation" and the "low speed rotation" each have a membership degree, and the membership degrees for performing the "medium speed rotation" are higher after the combination. A feedback optimization threshold is preset to represent a decision criterion that the system needs to perform feedback optimization. If the feedback optimization index data is lower than the threshold value, the system considers that feedback optimization is not needed, and device control adjustment is carried out through the controller based on the monitoring adjustment data. If the feedback optimization index data is higher than or equal to the threshold value, the system considers that the current state is better, and the water body restoration operation data is transmitted to the fuzzy logic control model to carry out optimal control strategy fuzzy reasoning. And transmitting the water body repair operation data to a fuzzy logic control model to perform optimal control strategy fuzzy reasoning. And carrying out fuzzy inversion processing according to the optimal fuzzy inference control data to obtain optimal control instruction data. For example, a control command of "medium speed rotation" is derived by fuzzy reasoning. The optimal control instruction data is transferred to the controller. And the controller performs operation optimization adjustment according to the optimal control instruction data. For example, parameters such as the depth of the lifting motor, the rotational speed of the water pumping motor, etc. are adjusted. The controller forms an intelligent closed-loop control strategy by monitoring the adjustment data in real time, so that intelligent optimization operation of the water body restoration system is realized.

Preferably, the feedback control algorithm formula in step S42 is as follows:

；

In the method, in the process of the invention, Represented as feedback optimization index data,Expressed as a standard deviation of the dissolved oxygen content of the deep water body,Represented as the duration of rotation data,Expressed as data on the dissolved oxygen content of the aqueous layer,Expressed as a target value of the dissolved oxygen content of the aqueous layer,Expressed as the lowest standard value of the dissolved oxygen content of the deep water body,Represented as the lowering depth data of the lifting motor,Represented by a natural constant which is a function of the natural constant,Expressed as the average value of the dissolved oxygen content of the deep water body,Represented as current device power data,Represented as device maximum power data,Expressed as the rotational speed data of the water pumping motor,The time value required by the water pumping motor to rotate for one circle is expressed,Representing the amount of power consumed by the pump motor to rotate one turn.

The invention utilizes a feedback control algorithm, and the algorithm formula fully considers the standard deviation value of the dissolved oxygen content of the deep water bodyData of rotation time lengthData on dissolved oxygen content of aqueous layerTarget value of dissolved oxygen content of aqueous layerMinimum standard value of dissolved oxygen content of deep water bodyDescending depth data of lifting motorNatural constantAverage value of dissolved oxygen content in deep waterCurrent device power dataMaximum electric quantity data of deviceRotational speed data of water pumping motorTime value required by one circle of rotation of water pumping motorElectric quantity consumed by one circle of rotation of water pumping motorAnd interactions between functions to form a functional relationship:

That is to say, By time of dayWhich means that from time zero to time is taken into accountCalculates a comprehensive optimization index。Is part of a normal distribution, here used to normalize the probabilities such that the result of the overall formula is a probability value. Sigma represents the standard deviation of the dissolved oxygen content of the deep water body, and measures the variability of the dissolved oxygen content. During the optimization process, we want to reduce this variability to maintain the stability of the water quality. Sub-itemsReflectingAnd (3) withThe ratio between them, and by the nature of the logarithmic function, the difference can be amplified, making the optimization algorithm more sensitive to the deviation between the dissolved oxygen content and the target value. Meanwhile, in denominatorThe influence of the lowest standard value of the dissolved oxygen content of the deep water body and the descending depth of the lifting motor on the dissolved oxygen distribution is considered. When (when)When the value of (c) is changed, it changes the distribution of the dissolved oxygen content, thereby affecting the performance of the whole system. Sub-itemsIs part of a Gaussian function that gives timeAt average valueThe nearby values are weighted higher because the most optimal operation generally occurs near the average. Sub-itemsThe square root of the ratio of the device charge to the maximum charge is expressed, and the larger this ratio is, the closer the current charge is to the maximum value, the better the system operation state is. Sub-itemsConsider the rotational speed of the pump motorTime required for one turnAnd the amount of electricity consumed. This partial expression evaluates the efficiency of the pump motor and thus provides a quantified efficiency and energy consumption indicator to ensure that the motor maintains optimal efficiency and minimum energy consumption under different operating conditions.

The method has the beneficial effects that detailed data of the target water body restoration area including terrain, water quality, water flow and the like are utilized to realize more comprehensive understanding. Through self-adaptive hydrologic parameter acquisition, the system can carry out dynamic adjustment according to real-time data and regional characteristics, ensures the accuracy of hydrologic parameters. And performing outlier correction processing on the self-adaptive hydrologic sampling data to further improve the data reliability. By establishing a water body fluid dynamic model, the flow characteristics of the water body are revealed by considering factors such as topography, water flow speed and the like, and important information is provided for restoration. The dissolved oxygen transmission simulation predicts the distribution condition of oxygen through a model, and is helpful for evaluating the oxygen saturation of the water body. The division treatment of the water body repair layer determines the vertical structure of the water body by dividing the water body layers with different depths, and improves the identification accuracy of the problem area. And transmitting target repair data to the controller in real time, so that the real-time performance and accuracy of the repair process are ensured, and the controller can make accurate repair decisions based on the latest data. And the repair operation data is transmitted back to the terminal equipment, so that the visibility and transparency of the repair process are improved. Extracting key operational parameters facilitates in-depth understanding of the characteristics of the actual repair operation. By constructing the fuzzy logic control model, the system can intelligently respond to variable environmental factors, and the self-adaptive adjustment of complex environmental changes is realized. The application of the fuzzy logic control model enables the system to adjust the control strategy in real time to adapt to the changing repair requirement, so as to achieve the optimal repair effect. The method has the advantages that the strong water body three-dimensional circulation is formed in the water body through the micro-lifting diversion technology, the dissolved oxygen content of the deep water body is improved, the water pumping motor is optimally controlled by adopting the closed-loop control strategy, the achievement of the standard of the dissolved oxygen content of the deep water body under the conditions of low cost and low energy consumption is realized, the method is suitable for repairing black and odorous water bodies, the black and odorous water bodies after treatment can be prevented from being treated again, and the method is a powerful water ecological repairing measure.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The utility model provides a can carry out aquatic ecology restoration method of water layering regulation, its characterized in that is applied to aquatic ecology restoration device, aquatic ecology restoration device includes elevator motor control module, water pumping motor drive module, water conservancy diversion structure module, telemetry terminal and water treatment module, and elevator motor control module includes elevator motor, controller, dissolved oxygen sensor array, velocity of flow direction appearance and temperature sensor, and dissolved oxygen sensor array, velocity of flow direction appearance, temperature sensor and water conservancy diversion structure module middle honeycomb duct one end fixed mounting is on elevator motor, aquatic ecology restoration method that can carry out water layering regulation includes the following steps:

2. The method for water ecological restoration capable of performing layered adjustment of a water body according to claim 1, wherein the step S1 comprises the steps of:

step S11: acquiring target water body restoration area data;

3. The method of claim 2, wherein the adaptive hydrographic sampling data comprises static hydrographic sampling data and dynamic hydrographic sampling data, and step S14 comprises the steps of:

4. A method of water ecological restoration capable of layered adjustment of a body of water as recited in claim 3, wherein step S147 includes the steps of:

5. The method for water ecological restoration capable of performing layered adjustment of a water body according to claim 2, wherein the step S2 comprises the steps of:

6. The method for water ecological restoration capable of performing layered adjustment of a body of water according to claim 5, wherein step S3 comprises the steps of:

7. The method for water ecological restoration capable of performing layered adjustment of a water body according to claim 6, wherein the formula of the lift control algorithm in step S32 is as follows:

；

8. The method of claim 6, wherein step S4 comprises the steps of:

9. The method for water ecological restoration capable of performing layered adjustment of a water body according to claim 8, wherein the feedback control algorithm formula in step S42 is as follows:

；

10. A water ecology restoration system capable of performing water stratification adjustment, for performing the water ecology restoration method capable of performing water stratification adjustment of claim 1, comprising: