Urban water environment ecological chain type repair system and method thereof

Abstract

This invention discloses an ecological chain restoration system and method for urban water environment, relating to the field of urban water environment restoration technology. The system comprises the following components: a monitoring unit, a data processing and analysis unit, a decision generation module, and a restoration execution unit. By real-time monitoring of water quality, biological community structure, and environmental parameter changes in the urban water environment, and utilizing machine learning algorithms for in-depth analysis and processing of the monitoring data, this invention can accurately identify problems and potential risks in the water environment. Based on these analysis results, the system can automatically generate restoration strategy adjustment instructions for the current water environment status, thereby achieving intelligent and precise restoration of the urban water environment.

Description

Technical Field

[0001] This invention relates to the field of urban water environment restoration technology, specifically to an urban water environment ecological chain restoration system and method. Background Technology

[0002] As an important component of the urban ecosystem, the health of the urban water environment directly affects the quality of life of urban residents, the stability of the urban ecosystem, and sustainable development. With the acceleration of urbanization, the urban water environment is facing unprecedented pressure and challenges, such as increasingly prominent problems of water pollution, imbalance of biological community structure, and degradation of ecological functions.

[0003] Traditional urban water environment restoration technologies have shortcomings, as they often fail to accurately identify existing problems and potential risks in the water environment.

[0004] In summary, traditional water environment restoration methods have significant shortcomings and cannot meet the urgent needs of current urban water environment restoration work. Therefore, it is particularly important to develop an urban water environment ecological chain restoration system and its methods. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide an urban water environment ecological chain restoration system and method, which can achieve [the following].

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an urban water environment ecological chain restoration system and method, the system comprising the following components: a monitoring unit, a data processing and analysis unit, a decision generation module, and a restoration execution unit;

[0007] The monitoring unit is responsible for real-time monitoring of water quality, biological community structure, and environmental parameter changes in the urban water environment. The monitoring unit includes a water quality sensor group, a biological monitoring module, and an environmental parameter monitor. The water quality sensor group includes sensors for detecting conventional water quality indicators as well as sensors for specific pollutants. The biological monitoring module uses image recognition technology combined with gene sequencing technology to monitor and analyze the microbial community, aquatic plant species and distribution density, and aquatic animal population structure in the water body. The environmental parameter monitor monitors meteorological data and hydrogeographic parameters of the restoration area in real time.

[0008] The data processing and analysis unit: analyzes and processes the data collected by the monitoring unit using a machine learning algorithm and generates repair strategy adjustment instructions. The machine learning algorithm employs an intelligent algorithm that combines hybrid neural networks and evolutionary algorithms; the algorithm formula is as follows: ,in For the predicted results, This is the input data for the neural network part. For the activation functions of neurons in a neural network, These are the weight parameters for the neural network part. This is the input data for the evolutionary algorithm. This refers to the fitness function in evolutionary algorithms. These are the weight parameters for the evolutionary algorithm. This is the balance coefficient;

[0009] The decision generation module: Based on the analysis results of the above algorithm and combined with the preset repair strategy library, automatically generates repair strategy adjustment instructions for the current water environment condition;

[0010] The restoration execution unit adjusts and optimizes the microbial community, aquatic plant community, hydrodynamic conditions, and ecological habitat according to the instructions of the data processing and analysis unit. The restoration execution unit includes a microbial restoration module, an aquatic plant restoration module, a hydrodynamic regulation module, and an ecological habitat construction and maintenance module.

[0011] Furthermore, the specific pollutant sensors in the water quality sensor group are integrated using microfluidic chip technology. The microfluidic chip has multiple microchannels built inside, and each microchannel is designed with a specific identification and detection unit for a specific pollutant. For the heavy metal ion detection channel, the selective adsorption and quantitative detection of specific heavy metal ions are achieved by using ion exchange resin fixation combined with electrochemical detection electrodes. The size of the microchannel is optimized through hydrodynamic simulation to ensure that the flow state of the sample in the channel is stable and the detection reaction is fully carried out. At the same time, the chip is modular, which can easily replace and upgrade the sensor units, improve the sensor group's adaptability to the detection of various new pollutants, and provide a comprehensive and flexible detection method for accurate monitoring.

[0012] Furthermore, the image recognition technology of the biological monitoring module is based on a deep neural network architecture with multi-scale feature fusion. At the network front end, convolutional kernels of different scales are used to process images in parallel to extract multi-scale features from microscopic biological cell structures to macroscopic biological community distribution. Then, these multi-scale features are fused through a feature fusion layer to enhance the expressive ability of biological image features. During network training, a combination of transfer learning and incremental learning is adopted. First, pre-training is performed using a large-scale general biological image dataset. Then, incremental training is performed using a small number of labeled aquatic biological images, tailored to the characteristics of aquatic biological images, to quickly and accurately adjust network parameters. This enables the network to effectively identify changes in biological species and community characteristics under different aquatic environments, providing an accurate data foundation for ecosystem health assessment.

[0013] Furthermore, the neuron activation function in the neural network part of the machine learning algorithm core of the data processing and analysis unit... Using piecewise function form: ,in The parameters are determined by analyzing the distribution characteristics of different types of water quality data. For water quality indicators that change linearly in the low concentration range, a linear function is used; for indicators that change nonlinearly in the medium concentration range, a quadratic function is used; and for indicators that change relatively slowly in the high concentration range, a logarithmic function is used. By using a piecewise activation function, the neural network can more accurately fit the changing patterns of water quality data in different concentration ranges, thereby improving the accuracy of the algorithm in predicting water quality changes.

[0014] Furthermore, the microbial cultivation reactor in the microbial remediation module of the remediation execution unit adopts a gradient temperature and gradient nutrient supply system. The reactor is equipped with multiple temperature zones, which are separated by heat-conducting insulating materials. The temperature of each zone is controlled by independent heating and cooling devices, forming a gradient temperature environment from low to high temperature to meet the temperature requirements of different microbial strains at different growth stages. At the same time, the nutrient supply system adopts a multi-pipe stratified supply method, with different pipelines delivering different types and concentrations of nutrients. According to the distribution location and growth stage of the microorganisms in the reactor, the type and concentration of nutrients supplied are precisely controlled to promote the efficient cultivation and specific functional enhancement of microorganisms, providing suitable microbial agents to cope with different pollution conditions.

[0015] Furthermore, the aquatic plant maintenance equipment in the aquatic plant restoration module of the restoration execution unit adopts an intelligent lighting and carbon dioxide replenishment system. The system monitors the light intensity and duration on the water surface in real time through a light sensor. When the light intensity is lower than the optimal light requirements for aquatic plant growth, the intelligent lighting system automatically activates the supplemental lighting. The spectrum and light intensity of the supplemental lighting are adjusted according to the photosynthetic characteristics of different aquatic plants to improve the absorption and purification capacity of aquatic plants for nutrients in the water.

[0016] Furthermore, the agitator in the hydrodynamic control module of the repair execution unit adopts magnetic levitation shaftless stirring technology. The rotating components inside the agitator are suspended in the water by magnetic levitation technology, without the traditional mechanical shaft connection, which reduces mechanical friction and energy loss and improves stirring efficiency. At the same time, the rotation speed and stirring direction of the agitator can be precisely controlled according to the instructions of the data processing and analysis unit. The driving and control of the stirring components can be achieved by changing the magnetic field strength and direction. In terms of the design of the stirring blades, the bionic principle is adopted, and the shape and movement trajectory of the blades are designed to mimic the swimming posture of aquatic animals, so that the water flow generated during the stirring process is more gentle and has a good turbulence effect. This can promote the full contact between pollutants, microorganisms and aquatic plants, reduce mechanical damage to aquatic organisms, and optimize the hydrodynamic control effect.

[0017] Furthermore, the ecological habitat construction and maintenance module of the restoration execution unit utilizes 3D printing technology for customized manufacturing. Based on the topography, water flow conditions, and biological community needs of different water areas, 3D printing technology is used to create ecological habitat models with complex internal structures and different surface roughness. In terms of maintenance, non-destructive testing of the ecological habitat structure is carried out regularly to promptly detect damage or blockages. Utilizing the repairability of 3D printing, damaged parts can be quickly manufactured and replaced to ensure the long-term stability and effectiveness of the ecological habitat, and promote the restoration and maintenance of biodiversity.

[0018] On the other hand, a method for ecological chain restoration of urban water environment is characterized by the following specific steps:

[0019] S1, Monitoring Steps: Real-time monitoring of water quality, biological community structure, and environmental parameter changes in the urban water environment. The monitoring unit includes a water quality sensor group, a biological monitoring module, and an environmental parameter monitor. The water quality sensor group includes sensors for detecting conventional water quality indicators and sensors for specific pollutants. The biological monitoring module uses image recognition technology combined with gene sequencing technology to monitor and analyze the microbial community, aquatic plant species and distribution density, and aquatic animal population structure in the water body. The environmental parameter monitor monitors meteorological data and hydrogeographic parameters of the restoration area in real time.

[0020] S2, Data Processing and Analysis Steps: The data collected by the monitoring unit is analyzed and processed using machine learning algorithms to generate repair strategy adjustment instructions. The machine learning algorithm employs an intelligent algorithm that combines hybrid neural networks and evolutionary algorithms. The algorithm formula is as follows: ,in For the predicted results, This is the input data for the neural network part. For the activation functions of neurons in a neural network, These are the weight parameters for the neural network part. This is the input data for the evolutionary algorithm. This refers to the fitness function in evolutionary algorithms. These are the weight parameters for the evolutionary algorithm. This is the balance coefficient;

[0021] S3, Decision generation step: Based on the analysis results of the above algorithm and combined with the preset repair strategy library, automatically generate repair strategy adjustment instructions for the current water environment condition;

[0022] S4, Repair Execution Steps: Adjust and optimize the microbial community, aquatic plant community, hydrodynamic conditions, and ecological habitat according to the instructions of the data processing and analysis unit. The repair execution unit includes a microbial repair module, an aquatic plant repair module, a hydrodynamic regulation module, and an ecological habitat construction and maintenance module.

[0023] Compared with existing technologies, this urban water environment ecological chain restoration system and method have the following beneficial effects:

[0024] I. This system monitors the water quality, biological community structure, and environmental parameter changes of the urban water environment in real time, and uses machine learning algorithms to perform in-depth analysis and processing of the monitoring data. It can accurately identify the problems and potential risks in the water environment. Based on these analysis results, the system can automatically generate adjustment instructions for the remediation strategy in response to the current water environment conditions, thereby achieving intelligent and precise remediation of the urban water environment.

[0025] Second, this system employs ecological restoration technologies and methods, including microbial restoration, aquatic plant restoration, hydrodynamic regulation, and the construction and maintenance of ecological habitats. These technologies and methods work together to form a complete ecological chain restoration system, which can comprehensively restore and optimize the urban water environment from multiple aspects. By adjusting and optimizing the microbial community, aquatic plant community, hydrodynamic conditions, and key ecological elements of ecological habitats, the system can effectively restore and enhance the ecological functions of the urban water environment, promote the restoration and maintenance of biodiversity, and thus build a healthier and more stable urban water ecosystem. This comprehensive ecological restoration method is of great significance for protecting and improving the urban water environment.

[0026] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0028] Figure 1 A flowchart illustrating the operation of an urban water environment ecological chain restoration system;

[0029] Figure 2 This is a flowchart illustrating a method for ecological chain restoration of urban water environments. Detailed Implementation

[0030] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below. Example

[0031] This embodiment describes the ecological chain restoration of urban landscape lakes.

[0032] Multiple monitoring points were set up in the urban landscape lake, and water quality sensor arrays were installed. Conventional water quality index sensors monitor water temperature, pH value, dissolved oxygen, chemical oxygen demand (COD), and ammonia nitrogen parameters. Specific pollutant sensors use microfluidic chip technology to detect heavy metals and organic pollutants that may be present in the lake. For example, through the ion exchange resin stationary phase and electrochemical detection electrode in the microfluidic chip, the concentration of copper ions in the lake water was accurately detected to be 0.05 mg / L and the concentration of lead ions to be 0.02 mg / L.

[0033] Using a biomonitoring module that combines image recognition technology with gene sequencing technology, the microbial community, aquatic plant species and distribution density, and aquatic animal population structure in lakes are monitored. Through a deep neural network architecture that integrates multi-scale features, changes in the distribution density of green algae and diatoms in the lake water, as well as changes in the population size of carp and crucian carp, are identified. Gene sequencing technology is used to analyze the microbial community structure, revealing that the abundance of some beneficial microorganisms is low.

[0034] The environmental parameter monitor monitors meteorological data and hydrogeographic parameters in real time. For example, it detected that when the summer temperature rises, the lake water level drops by 0.5 meters and the water flow speed slows down to 0.1 m / s.

[0035] The data acquisition and transmission subunit collects monitoring data and transmits it to the machine learning algorithm core. The machine learning algorithm core uses an intelligent algorithm that combines hybrid neural networks and evolutionary algorithms for analysis. ,in For the predicted results, This is the input data for the neural network part. For the activation functions of neurons in a neural network, These are the weight parameters for the neural network part. This is the input data for the evolutionary algorithm. This refers to the fitness function in evolutionary algorithms. These are the weight parameters for the evolutionary algorithm. To balance the coefficients, the input data for the neural network part includes water quality monitoring data. The neuron activation function adopts a piecewise function form, and the appropriate function form is selected for calculation according to the concentration range of different water quality indicators. The input data for the evolutionary algorithm part is biological community monitoring data, and the fitness function is set according to the health status and stability of the biological community.

[0036] Based on the pre-set remediation strategy library, the algorithm generates remediation strategy adjustment instructions according to the analysis results. For example, when it is found that the ammonia nitrogen concentration in the water is high and the abundance of beneficial microorganisms is low, the instruction is generated to increase the amount of nitrifying bacteria added to the microbial remediation module.

[0037] The microbial cultivation reactor employs a gradient temperature and gradient nutrient supply system to cultivate beneficial nitrifying bacteria within the reactor. The reactor is divided into temperature zones, with the low-temperature zone controlled at 20°C, the medium-temperature zone at 25°C, and the high-temperature zone at 30°C to promote microbial growth. Based on the microbial growth stage, a multi-channel, stratified supply method is used to provide high-concentration nitrogen nutrients in the initial stage, and then adjust to low-concentration nitrogen and appropriate amounts of carbon nutrients in the later stage.

[0038] The intelligent lighting and carbon dioxide replenishment system of the aquatic plant maintenance equipment activates supplemental lighting on cloudy days or when there is insufficient light, based on data from the light sensor. For lotus aquatic plants, the supplemental lighting spectrum is adjusted to a red to blue light ratio of 3:1, with a light intensity of 2000-3000 lux to meet their photosynthetic needs. At the same time, carbon dioxide is replenished regularly to promote the growth of aquatic plants, absorb nutrients from the water, and improve water quality.

[0039] The magnetic levitation shaftless mixer adjusts the mixing speed and direction according to instructions. In the high temperatures of summer, the mixing speed is increased to 100 rpm to enhance water flow, prevent local overheating and excessive algae growth. The mixing blades mimic the swimming posture of fish to improve mixing efficiency, promote contact between lake water and oxygen, and increase dissolved oxygen content.

[0040] 3D printing technology is used to customize and manufacture ecological habitats. Based on the lake topography and biological community needs, artificial reefs with porous structures and suitable roughness are printed and placed in the shallow waters of the lake to provide habitats and breeding grounds for fish. Underwater robots are used to conduct non-destructive testing on the ecological habitats regularly. When minor damage to the reefs is found, 3D printing technology is used to quickly manufacture and replace the damaged parts to ensure the stability of the ecological habitats. Example

[0041] This embodiment describes the ecological chain restoration of urban rivers.

[0042] A monitoring point is set up every 500 meters along the city's inner river. The conventional water quality index sensors in the water quality sensor group monitor the turbidity, conductivity, total phosphorus, and total nitrogen of the river water in real time. The specific pollutant sensors detect cyanide and volatile phenol pollutants that may be present in industrial wastewater. For example, the total phosphorus concentration in a certain section of the river was detected to be 0.3 mg / L and the total nitrogen concentration was 2.5 mg / L, which exceeded the Class IV standard for surface water.

[0043] The biomonitoring module uses image recognition technology to analyze changes in the coverage area of ​​aquatic plants, gene sequencing technology to monitor changes in the types and quantities of microorganisms that decompose organic pollutants in the microbial community, and to observe the activities of aquatic animals. It was found that the number of some microorganisms that can degrade organic pollutants in the microbial community was relatively small, which affected the self-purification capacity of the river water.

[0044] The environmental parameter monitor tracks changes in the water level and flow velocity of inland rivers, as well as surrounding meteorological data.

[0045] The data acquisition and transmission subunit transmits monitoring data to the core of the machine learning algorithm. ,in For the predicted results, This is the input data for the neural network part. For the activation functions of neurons in a neural network, These are the weight parameters for the neural network part. This is the input data for the evolutionary algorithm. This refers to the fitness function in evolutionary algorithms. These are the weight parameters for the evolutionary algorithm. As a balance coefficient, water quality monitoring data is input into the neural network, and the activation function of the neurons is calculated based on the characteristics of different indicators. ,in The parameters were determined by analyzing the distribution characteristics of different types of water quality data. The evolutionary algorithm was input with biological community monitoring data, and the fitness function was used to evaluate the contribution of the biological community to water purification.

[0046] Based on the algorithm analysis results and the repair strategy library, instructions are generated. For example, when it is found that total phosphorus and total nitrogen exceed the standard and there are insufficient degrading microorganisms, the instructions increase the application of specific microbial agents and adjust the aquatic plant planting strategy.

[0047] The microbial cultivation reactor cultivates microbial communities that can efficiently degrade organic pollutants and absorb nitrogen and phosphorus. Through a gradient temperature and gradient nutrient supply system, the reactor temperature is controlled between 22-28°C. High phosphorus and high nitrogen nutrients are provided in the early stage of microbial growth, and then adjusted to low phosphorus and low nitrogen nutrients in the later stage to promote the microorganisms to adapt to the inland river water environment and enhance their ability to degrade pollutants. Then, the cultivated microbial agents are released into the river water.

[0048] The aquatic plant maintenance equipment adjusts the light intensity based on the data from the light sensor. For duckweed, supplemental lighting is activated when there is insufficient light, and the light intensity is adjusted to 1500-2000 lux to promote its growth and reproduction. The aquatic plant planting area is rationally planned, and water hyacinth, a plant with strong absorption capacity, is planted in heavily polluted areas. Through the absorption of nitrogen and phosphorus nutrients in the water, the water quality is purified.

[0049] During the dry season, the magnetic levitation shaftless mixer accelerates the mixing speed to 80 rpm according to instructions, improving hydrodynamic conditions, increasing dissolved oxygen content in the water, promoting the decomposition of pollutants by microorganisms and the growth of aquatic plants. The shape and movement trajectory of the mixing blades mimic the swimming style of aquatic animals, improving the mixing effect and preventing sediment accumulation.

[0050] Using 3D printing technology, ecological habitats can be created based on the shape of inland river channels and the needs of organisms. For example, ecological bricks with complex cave structures can be printed in river bends to provide habitats and breeding grounds for aquatic animals such as river shrimp and crabs. The ecological habitats can be inspected regularly, and when ecological bricks are found to be blocked by silt, parts with the same structure can be manufactured using 3D printing technology to replace them, ensuring the normal function of the ecological habitats.

[0051] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An urban water environment ecological chain type repair system, characterized in that, The system comprises a monitoring unit, a data processing and analysis unit, a decision generation module and a repair execution unit: The monitoring unit: real-time monitoring of water quality, biological community structure and environmental parameter changes of urban water environment, the monitoring unit comprises a water quality sensor group, a biological monitoring module and an environmental parameter monitor, wherein the water quality sensor group covers sensors for detecting conventional water quality indicators, and sensors for specific pollutants, the biological monitoring module uses image recognition technology combined with gene sequencing technology to monitor and analyze microbial communities, aquatic plant species and distribution density, and aquatic animal population structure in water, and the environmental parameter monitor monitors meteorological data and hydrological geographical parameters of the repair area in real time; The data processing and analysis unit: through machine learning algorithm to analyze the data collected by the monitoring unit and generate repair strategy adjustment instruction, wherein the machine learning algorithm uses a hybrid neural network and evolutionary algorithm fusion intelligent algorithm, the algorithm formula is: Wherein is the predicted result, is the input data of the neural network part, is the neuron activation function of the neural network, is the weight parameter of the neural network part, is the input data of the evolutionary algorithm part, is the fitness function in the evolutionary algorithm, is the weight parameter of the evolutionary algorithm part, is the balance coefficient, the neuron activation function of the neural network part Adopting a piecewise function form: Wherein The parameters are determined by analyzing the distribution characteristics of different types of water quality data. For water quality indicators that change linearly in the low concentration range, a linear function is used. For indicators that have nonlinear change trends in the medium concentration range, a quadratic function is used. For indicators that change relatively slowly in the high concentration range, a logarithmic function is used. The decision generation module: according to the analysis results of the above algorithm, combined with the preset repair strategy library, automatically generates repair strategy adjustment instructions for the current water environment condition; The repair execution unit: adjusts and optimizes microbial communities, aquatic plant communities, hydrodynamic conditions and ecological habitats according to the instructions of the data processing and analysis unit, the repair execution unit comprises a microbial repair module, an aquatic plant repair module, a hydrodynamic control module and an ecological habitat construction and maintenance module.

2. The urban water environment ecological chain type repair system according to claim 1, characterized in that, The specific pollutant sensor in the water quality sensor group is integrated by using microfluidic chip technology, multiple microchannels are constructed inside the microfluidic chip, each microchannel is designed with specific recognition and detection units for a specific pollutant, for heavy metal ion detection channel, selective adsorption and quantitative detection of specific heavy metal ions are realized by using ion exchange resin stationary phase combined with electrochemical detection electrode, the size of the microchannel is optimized through fluid mechanics simulation, and the chip adopts modularization, so that the replacement and upgrading of the sensor unit can be easily realized.

3. The urban water environment ecological chain repair system according to claim 1, characterized in that, The image recognition technology of the biological monitoring module is based on a deep neural network architecture with multi-scale feature fusion, at the front end of the network, different scale convolution kernels are used to process images in parallel to extract multi-scale features from microscopic biological cell structure to macroscopic biological community distribution, then the multi-scale features are fused through a feature fusion layer, in the network training process, a combination of transfer learning and incremental learning is used, first, a large-scale general biological image dataset is used for pre-training, then a small amount of labeled water environment biological images are used for incremental training according to the characteristics of water environment biological images, the network parameters are quickly and accurately adjusted, so that the network can effectively identify the changes of biological species and community characteristics in different water environments.

4. The urban water environment ecological chain repair system according to claim 1, characterized in that, The microbial cultivation reactor in the microbial remediation module of the remediation execution unit adopts a gradient temperature and gradient nutrient supply system. Multiple temperature zones are arranged in the reactor, and different zones are separated by heat conduction isolation materials. The temperature of each zone is controlled by independent heating and cooling devices, forming a gradient temperature environment from low temperature to high temperature. Meanwhile, the nutrient supply system adopts a multi-pipeline layered supply method. Different pipelines transport different types and concentrations of nutrients. According to the distribution position and growth stage of microorganisms in the reactor, the supply type and concentration of nutrients are accurately controlled to promote efficient cultivation and specific function enhancement of microorganisms.

5. The urban water environment ecological chain repair system according to claim 1, characterized in that, The aquatic plant maintenance equipment in the aquatic plant remediation module of the remediation execution unit adopts an intelligent lighting and carbon dioxide supplement system. The lighting intensity and time on the water surface are monitored in real time by a lighting sensor. When the lighting intensity is lower than the optimal growth lighting requirement of aquatic plants, the intelligent lighting system automatically starts the light supplement lamp. The spectrum and lighting intensity of the light supplement lamp are adjusted according to the photosynthesis characteristics of different aquatic plants.

6. The urban water environment ecological chain repair system according to claim 1, characterized in that, The stirrer in the hydrodynamic regulation module of the remediation execution unit adopts magnetic suspension shaftless stirring technology. The rotating parts inside the stirrer are suspended in the water body by magnetic suspension technology without traditional mechanical shaft connection. Meanwhile, the speed and direction of the stirrer can be accurately controlled according to the instructions of the data processing and analysis unit. The driving and regulation of the stirring parts are realized by changing the magnetic field strength and direction. In the design of stirring blades, the bionics principle is adopted to simulate the swimming posture of aquatic animals to design the shape and motion trajectory of the blades.

7. The urban water environment ecological chain repair system according to claim 1, characterized in that, The ecological habitat construction in the ecological habitat construction and maintenance module of the remediation execution unit adopts 3D printing technology for customized manufacturing. According to the terrain, water flow conditions and biological community requirements of different water areas, 3D printing technology is used to manufacture ecological habitat models with complex internal structure and different surface roughness. In terms of maintenance, regular non-destructive testing of the ecological habitat structure is performed to timely detect damage or blockage of the structure. The 3D printing repairability is used to quickly manufacture and replace damaged parts to ensure the long-term stability and effectiveness of the ecological habitat and promote the recovery and maintenance of biological diversity.

8. A method for repairing an urban water environment ecological chain, characterized in that, The specific steps of the method are as follows: S1, monitoring step: real-time monitoring of water quality, biological community structure and environmental parameter changes of urban water environment, the monitoring unit includes water quality sensor group, biological monitoring module and environmental parameter monitor, wherein the water quality sensor group covers sensors for detecting regular water quality indicators and sensors for specific pollutants, the biological monitoring module uses image recognition technology combined with gene sequencing technology to monitor and analyze microbial communities, aquatic plant species and distribution density, and aquatic animal population structure in the water body, and the environmental parameter monitor monitors meteorological data and hydrological geographical parameters of the remediation area in real time; S2, data processing and analysis step: analyzing and processing the data collected by the monitoring unit through a machine learning algorithm and generating a repair strategy adjustment instruction, wherein the machine learning algorithm uses an intelligent algorithm combining a hybrid neural network and an evolutionary algorithm, and the algorithm formula is: wherein is a prediction result, is input data of the neural network part, is a neuron activation function of the neural network, is a weight parameter of the neural network part, is input data of the evolutionary algorithm part, is a fitness function in the evolutionary algorithm, is a weight parameter of the evolutionary algorithm part, is a balance coefficient; S3, decision generation step: according to the analysis results of the above algorithm, combined with the preset remediation strategy library, automatically generate remediation strategy adjustment instructions for the current water environment condition; S4, repair execution step: adjusting and optimizing the microbial community, the aquatic plant community, the hydrodynamic condition and the ecological habitat according to the instruction of the data processing and analysis unit, the repair execution unit comprising a microbial repair module, an aquatic plant repair module, a hydrodynamic regulation module and an ecological habitat construction and maintenance module.

Images