Ecological conservation and purification accurate real-time monitoring system integrated with intelligent sensor

Abstract

The invention belongs to the technical field of ecological environment protection and intelligent environment monitoring, and particularly relates to an ecological conservation and purification accurate real-time monitoring system integrated with an intelligent sensor, which comprises a multi-parameter monitoring module, a self-balancing sensing terminal structure, a self-adaptive surface purification unit, an algorithm center and a logic compensation control unit, the self-adaptive surface purification unit scours and peels attachments by utilizing high-frequency vibration or directional fluid; the algorithm center constructs an evaluation model based on a biochemical parameter coupling relationship, and executes data authenticity depth verification; and the logic compensation control unit performs data correction and process optimization according to the verification result. By the adoption of the technical scheme, long-acting high-precision sensing and intelligent feedback control of water quality parameters under the complex habitat are achieved.

Description

Technical Field

[0001] This invention belongs to the field of ecological environment protection and intelligent environmental monitoring technology, specifically a precise real-time monitoring system for ecological conservation and purification that integrates intelligent sensors. Background Technology

[0002] Against the backdrop of global ecological civilization construction and comprehensive watershed water environment management, a refined and intelligent real-time monitoring system is the core for maintaining wetland ecological balance and improving water purification efficiency. Fluctuations in key biochemical parameters of ecologically conservation water bodies directly reflect the operating status of purification processes and ecological health. Monitoring systems integrating intelligent sensors are widely deployed to provide decision support. However, due to the special characteristics of ecologically conservation water body environments, existing monitoring systems face profound technical contradictions in terms of perception accuracy and operational stability.

[0003] Existing monitoring systems rely on probe-type sensors that need to be in direct contact with the water body. However, the strong biological activity of ecological conservation water bodies easily causes biofilms to form at the sensor probe interface. These biofilms decouple the sensor from the large water environment, resulting in the sensor sensing signal only reflecting the local concentration of metabolic products inside the biofilm, leading to serious systemic measurement bias.

[0004] Existing active cleaning devices for dealing with biofilm have significant performance conflicts. They can increase power consumption and shorten system cruise cycles, and are prone to mechanical failure and corrosion due to wetland environmental disturbances. Furthermore, the measurement deviations caused by biofilm are highly concealed, and existing systems lack self-diagnostic capabilities, which can easily lead to data distortion and erroneous intervention, resulting in energy waste and ecological imbalance. The core technical challenge is to build an intelligent monitoring system that can isolate biofilm, achieve long-term self-maintenance, and provide data verification and compensation.

[0005] Therefore, the present invention provides a precise real-time monitoring system for ecological conservation and purification that integrates intelligent sensors. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by the present invention to solve its technical problem is: the present invention provides an integrated intelligent sensor-based ecological conservation and purification precision real-time monitoring system, which includes a sensing terminal hardware system, a data processing algorithm hub, and a closed-loop feedback control link. The sensing terminal hardware system includes a multi-parameter monitoring module. The multi-parameter monitoring module integrates a dissolved oxygen sensor, a chemical oxygen demand sensor, a total phosphorus sensor, and an ammonia nitrogen sensor. Each sensor is fixed in a modular housing with anti-corrosion properties through an integrated packaging process. The sensing windows are uniformly exposed in the spatial hierarchy to ensure the synchronicity of each biochemical parameter collection point in the water flow field. The dissolved oxygen sensor performs real-time capture of the partial pressure of dissolved oxygen molecules in the water. The chemical oxygen demand (COD) sensor uses ultraviolet absorption spectroscopy of a specific wavelength or electrochemical oxidation principle to obtain the characteristics of organic matter load in water bodies; the total phosphorus sensor and ammonia nitrogen sensor respectively perform quantitative sensing of nutrient components. The sensing window surface of each sensor is equipped with an anti-biofouling coating to delay the initial attachment of microorganisms and algae in the primary stage of the physical layer.

[0008] The multi-parameter monitoring module is equipped with a high-precision signal conditioning unit. This unit performs the conversion logic from analog signal to digital feature stream. The signal conditioning unit performs low-pass filtering and dynamic gain compensation on the charge fluctuation, photocurrent intensity, or potential difference signals captured by the sensor to eliminate random noise caused by suspended particulate matter and electromagnetic environment in the ecological conservation water body, ensuring the linearity and resolution of the original sensing data. The digital feature stream is transmitted back to the data processing algorithm center through the shielded transmission bus, providing data support for subsequent in-depth verification of data authenticity.

[0009] To address the complex physical conditions of wetlands in ecological conservation areas, the system described in this invention is equipped with a self-balancing sensing terminal structure. This structure utilizes the principle of center of gravity compensation and a symmetrical layout based on fluid dynamics to ensure that the multi-parameter monitoring module maintains a stable sensing posture in water environments containing emergent plant debris, plankton, and adhesive humus. The self-balancing sensing terminal structure is externally equipped with a protective grid. The grid bars of the protective grid are designed with a non-uniform distribution to physically block the direct impact of large-sized plant debris on the sensing window, while ensuring sufficient exchange of the water flow field at the sensing interface.

[0010] To address the measurement discrepancies caused by biofilm growth, the system described in this invention is equipped with an adaptive surface cleaning unit. This adaptive surface cleaning unit executes a long-term self-maintenance logic for the sensing window interface. Its physical mechanism involves using controlled high-frequency vibration waves or directional fluid scouring to generate microscale shear force, thereby disrupting the quasi-closed physical microenvironment constructed by the biofilm at the sensing interface. The adaptive surface cleaning unit and the multi-parameter monitoring module are spatially integrated and coupled. When the system detects a risk of biofilm adhesion or reaches a preset maintenance cycle, the adaptive surface cleaning unit is triggered. During the purification process, the high-frequency vibration module performs resonant excitation on the sensing window and its surrounding structure, causing the initially attached algae, bacterial spores and fungi to peel off. The directional fluid flushing module utilizes the principle of localized jet flow to expel detached biological deposits from the microenvironment of the sensor's material exchange interface. This non-contact purification method, supplemented by low mechanical disturbance, avoids the physical entanglement failure and motor stalling damage that are prone to occur in traditional mechanical brushing in complex wetland habitats, ensuring the stability of purification efficiency during long-term operation.

[0011] Preferably, the system of the present invention is configured with an energy management subsystem to address the energy robustness requirements of distributed monitoring stations. The energy management subsystem executes a dynamic power allocation strategy based on workload. Its internal logic unit coordinates and optimizes the sleep-wake frequency of sensing terminals, the operating intensity of adaptive surface purification units, and the data transmission frequency. In the stable range of water quality parameter fluctuations, the energy management subsystem reduces the system's cruise power consumption. When abnormal parameter fluctuations are detected or a trend of biofouling is identified, the system automatically increases the energy input priority of the adaptive surface purification units. This dynamic power adjustment logic ensures that the system has a longer cruise cycle in wild habitats lacking stable power grid support.

[0012] Preferably, the data processing algorithm core is equipped with a data authenticity deep verification algorithm module. This module executes the authenticity deep verification logic for the data returned from the sensing layer, aiming to identify hidden measurement deviations caused by the slow growth of biofilm. The data authenticity deep verification algorithm module uses the biochemical coupling relationship between parameters such as dissolved oxygen, chemical oxygen demand, total phosphorus, and ammonia nitrogen to construct a correlation evaluation model. Specifically, this module calculates the cross-correlation coefficients and their change trajectories between various biochemical parameters in real time. When the dissolved oxygen value continues to decrease while the chemical oxygen demand index remains constant or fluctuates in the opposite direction, the algorithm module executes anomaly discrimination logic to distinguish whether this change is due to the evolution of the actual purification process operation state or due to the increase in material diffusion resistance caused by the thickening of the biofilm on the sensor probe surface. This module uses the gradual characteristics of the zero-point drift caused by biofilm attachment in the time domain to establish a logical mapping of the biofilm growth kinetic equation and scores the confidence of the measurement data. If the score is lower than a preset threshold, the algorithm module determines that the sensing layer has caused source distortion.

[0013] Preferably, the system of the present invention is equipped with a logic compensation control unit. This unit receives the judgment result of the data authenticity deep verification algorithm module and executes dynamic correction logic for the closed-loop control command. When the sensor detects perception distortion caused by biofilm interference, the logic compensation control unit generates a logic compensation factor to perform deconvolution compensation or weighted correction on the original sensing value, eliminating the shading effect of biofilm on the material exchange interface. The corrected accurate data stream is input to the closed-flow control algorithm to guide subsequent automated aeration adjustment, aquatic plant harvesting cycle planning, or emergency pesticide dosing execution commands. Through this logic compensation mechanism, the system avoids blind intervention caused by data distortion and ensures that actuators such as aeration equipment are not misled by falsely low dissolved oxygen values.

[0014] Preferably, the logic compensation control unit also executes global optimization logic for the ecological conservation and purification process. This unit uses real-time biochemical parameters obtained by the multi-parameter monitoring module, combined with the ecosystem purification efficiency model, to predict the decline trend of purification capacity. Through the data after logic compensation, the logic compensation control unit accurately controls the timing of aquatic plant harvesting to prevent secondary pollution caused by the decay of plant remains. In emergency situations, the decision instructions generated by the logic compensation control unit have protective constraints on the fragile dynamic balance of biological populations in the ecological conservation system. By controlling the intensity of physical disturbance and the dosage of pesticides, the habitat stability of the ecological conservation water body is maintained.

[0015] Preferably, when performing dissolved oxygen sensing, the multi-parameter monitoring module of the present invention utilizes the fluorescence quenching principle to reduce the consumption of oxygen molecules during the sensing process, thereby reducing the concentration gradient deviation between the microenvironment of the sensing window and the external large water body environment. Combined with the flushing logic of the adaptive surface purification unit, the sensing accuracy is further improved. In chemical oxygen demand monitoring, the system uses multi-wavelength spectral compensation logic to eliminate the interference of water turbidity and suspended matter on absorbance, ensuring the data quality of organic matter load sensing. When performing total phosphorus and ammonia nitrogen sensing, the system is equipped with a microfluidic pretreatment unit to perform pre-filtration of complex impurities in wetland water and precise ratio of reaction reagents, ensuring the chemical conversion accuracy of nutrient monitoring.

[0016] Preferably, the data authenticity deep verification algorithm module also integrates the logic for tracing the source of perceived failures. When the system determines that a certain parameter has experienced irreversible zero-point drift or perceived failure, the module automatically triggers a sensor health warning signal and pushes a diagnostic package containing the root cause of failure (such as irreversible biofilm formation, probe electrochemical corrosion, or exogenous damage to the mechanical structure) to the operation and maintenance terminal through the data transmission link, thereby realizing the transformation of the operation and maintenance paradigm from post-maintenance to predictive maintenance.

[0017] Preferably, the system of the present invention is further configured with a habitat stability assessment unit in the processing layer. This unit uses the rate of change of biochemical parameters fed back by the multi-parameter monitoring module to assess the impact of the current monitoring intervention action (such as aeration intensity, hydraulic load adjustment) on the stability of bottom sediments and the distribution of microbial populations by calculating information entropy or system disturbance index. If the assessment results show that the intervention action may break the fragile balance of the system, the logic compensation control unit will automatically perform smoothing processing of the intervention intensity to ensure that the improvement of purification efficiency does not come at the expense of habitat stability.

[0018] The beneficial effects of this invention are as follows: 1. The present invention discloses an integrated intelligent sensor-based ecological conservation and purification precision real-time monitoring system. Through multi-parameter monitoring modules integrating dissolved oxygen, chemical oxygen demand, total phosphorus, and ammonia nitrogen sensors, it achieves simultaneous acquisition of all biochemical factors in the ecological conservation and purification process, providing a complete set of underlying features for precise real-time monitoring. 2. The ecological conservation and purification precision real-time monitoring system integrating intelligent sensors described in this invention ensures the physical cleanliness and structural reliability of the sensing hardware under complex wetland conditions through the coordinated operation of a self-balancing sensing terminal structure and an adaptive surface purification unit. By utilizing a non-contact purification mechanism, it effectively suppresses the initial adhesion and gradual biofilm formation on the sensing interface, resolving the measurement failure contradiction caused by biofilm adhesion in long-term immersion environments. Simultaneously, it significantly reduces maintenance frequency and system power consumption. 3. The ecological conservation and purification precision real-time monitoring system integrating intelligent sensors described in this invention, by establishing a cross-correlation evaluation model between biochemical parameters, possesses the ability to self-diagnose the authenticity of the sensed data. It can accurately distinguish between real parameter fluctuations caused by process evolution and spurious drift caused by probe aging and biofilm formation, eliminating the source bias in the discrimination logic. 4. The integrated intelligent sensor-based ecological conservation and purification precision real-time monitoring system described in this invention ensures the scientific validity and accuracy of feedback control commands through real-time dynamic correction of the sensed data. This feature avoids energy waste and excessive physical disturbance caused by data distortion, prevents disruption of the dynamic balance of the ecosystem due to misjudgment, and ensures that the ecological conservation and purification process always operates in the optimal state of water body restoration. 5. The ecological conservation and purification precision real-time monitoring system integrating intelligent sensors described in this invention improves the system's operational robustness in remote and distributed environments through the logical nesting of the energy management subsystem and various functional modules. It constructs a low-power, highly reliable, and self-maintaining intelligent monitoring system that fully meets the core requirements of precise and long-term real-time monitoring in the ecological conservation and purification process. Attached Figure Description

[0019] The invention will now be further described with reference to the accompanying drawings.

[0020] Figure 1 This is a structural block diagram of an ecological conservation and purification precision real-time monitoring system integrating intelligent sensors, as described in this invention. Detailed Implementation

[0021] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0022] like Figure 1 As shown in the figure, an integrated intelligent sensor-based ecological conservation and purification precision real-time monitoring system of the present invention includes a sensing terminal hardware system, a data processing algorithm hub, and a closed-loop feedback control link. The sensing terminal hardware system serves as the physical contact point for the system to acquire external biochemical information and performs direct contact sensing of complex wetland habitats in specific engineering implementations. The sensing terminal hardware system includes a multi-parameter monitoring module, which serves as the physical carrier of the sensor cluster. In a specific application scenario, a dissolved oxygen sensor, a chemical oxygen demand sensor, a total phosphorus sensor, and an ammonia nitrogen sensor are integrated. Furthermore, in order to ensure structural stability in long-term immersion environments, each sensor is fixed in a modular housing with corrosion-resistant properties through integrated packaging technology. The shell is preferably made of 316L stainless steel or polytetrafluoroethylene composite material with high chemical stability to resist the oxidation and corrosion of metal substrate by humus, acid and alkali fluctuations and dissolved oxygen in the ecological conservation water body. In this invention, the sensing windows of each sensor are uniformly exposed in spatial order. This design not only ensures the synchronicity of each biochemical parameter collection point in the water flow field, but also eliminates the monitoring phase difference caused by the discrete distribution of sensors, ensuring that the collected multiple parameters have extremely high spatial correlation consistency within the same time slice.

[0023] The dissolved oxygen sensor integrated in the multi-parameter monitoring module uses the fluorescence quenching principle to capture the partial pressure of dissolved oxygen molecules in water in real time. Specifically, the sensor is equipped with an indicator layer that generates fluorescence upon excitation. Fluorescence signals are produced by blue light excitation. Since oxygen molecules have a specific quenching effect on fluorescence, the system reverse-engineers the dissolved oxygen concentration in the water by measuring the decay curve of fluorescence lifetime or intensity. This detection method does not consume oxygen molecules, thus reducing the concentration gradient deviation between the sensing window microenvironment and the external large water body environment. The chemical oxygen demand (COD) sensor utilizes multi-wavelength ultraviolet absorption spectroscopy logic to obtain the organic matter load characteristics of the water body. By establishing an absorption model of specific organic functional groups in the ultraviolet band, non-contact sensing of COD is achieved. Multi-wavelength spectral compensation logic is used to eliminate interference from water turbidity and suspended matter on absorbance. The total phosphorus sensor and ammonia nitrogen sensor perform quantitative sensing of nutrient components. They are equipped with a microfluidic pretreatment unit to perform pre-filtration of complex impurities in wetland water and precise ratio of reaction reagents, ensuring the accuracy of chemical conversion in nutrient monitoring. Subsequently, a colorimetric reaction combined with colorimetric detection principles converts the chemical concentration into a photoelectric signal.

[0024] At the sensing front end of the system of the present invention, the sensing window surface of each sensor is equipped with an anti-biofouling coating. The anti-biofouling coating adopts nano-scale hydrophobic material or polyethylene glycol modification layer. By reducing the surface energy of the sensing interface, it delays the initial attachment of microorganisms, algae and adhesive extracellular polymers in the initial stage of the physical layer, and reserves logical buffer space for subsequent purification actions. The multi-parameter monitoring module is further equipped with a high-precision signal conditioning unit. This unit, as the core of the analog signal to digital feature stream conversion logic, integrates an operational amplifier, a gain controller, and a high-bit analog-to-digital converter. The signal conditioning unit performs low-pass filtering on the weak charge fluctuations, photocurrent intensity, or potential difference signals captured by the sensor. By setting the cutoff frequency, it filters out high-frequency random noise in the ecological conservation water body caused by the impact of tiny bubbles, the passage of particulate matter through the sensing surface, and the external radio environment. Meanwhile, the high-precision signal conditioning unit performs dynamic gain compensation, automatically adjusting the quantization resolution according to the dynamic range of the input signal to ensure that the original sensing data has excellent linearity and resolution across the entire range.

[0025] To address the common problems of sensor displacement, flipping, and silt burial in complex wetland physical conditions in ecological conservation areas, the present invention is equipped with a self-balancing sensing terminal structure. This structure utilizes the principle of center of gravity compensation, by configuring a heavy metal counterweight at the bottom of the multi-parameter monitoring module, and by using a hollow structure to adjust the relative position of the geometric center and the buoyancy center. Combined with a fluid dynamic symmetrical layout design, it ensures that the multi-parameter monitoring module maintains a stable sensing posture in water environments containing emergent plant remains, plankton, and adhesive humus, i.e., the sensing window always points towards the direction of the main current field to be measured. Furthermore, the self-balancing sensing terminal structure is externally equipped with a protective grid. The grid bars of the protective grid are designed with a non-uniform distribution. The physical logic is as follows: larger gaps are used in the direction of water flow to ensure sufficient flow field exchange, while finer gaps are used in the side where debris is easily impacted. This aims to physically block large plant debris and plastic waste from directly impacting the sensing window, preventing mechanical damage, while using the grid bars to rectify the water flow and form a stable laminar flow environment at the front end of the sensing window.

[0026] To address the contradiction between biofilm growth and zero-point drift caused by strong biological activity in ecological conservation water bodies, this invention system is equipped with an adaptive surface purification unit. The adaptive surface purification unit executes a long-term self-maintenance logic for the sensing window interface. Its core physical mechanism lies in using controlled high-frequency vibration waves or directional fluid scouring to generate microscale shear force. This shear force can effectively destroy the quasi-closed physical microenvironment constructed by the biofilm at the sensing interface, preventing the generation of false features such as low dissolved oxygen or high ammonia nitrogen in the sensing area due to microbial aggregation. The adaptive surface purification unit and the multi-parameter monitoring module are spatially integrated and coupled through a piezoelectric ceramic transducer or a micro submersible pump array.

[0027] When the system detects a risk of bioattachment or reaches a preset maintenance cycle, the adaptive surface cleaning unit is triggered. During the cleaning process, the high-frequency vibration module performs resonance excitation on the sensing window and its surrounding structure. For example, the vibration frequency is set between 25 kHz and 40 kHz, which causes the initially attached algae, bacterial spores and fungi to peel off by generating cavitation effect and local high-frequency reciprocating shear. Meanwhile, the directional fluid flushing module utilizes the principle of local jetting to spray water with a certain kinetic energy through nozzles, quickly expelling the detached biological attachments from the material exchange interface microenvironment of the sensor. This non-contact purification method, supplemented by low mechanical disturbance, completely avoids the physical entanglement failure and motor stalling damage that are prone to occur in traditional mechanical brushing in complex wetland habitats, ensuring the stability of purification efficiency during long-term operation. Furthermore, the unit can automatically adjust the frequency and intensity of purification actions based on environmental characteristics such as water temperature and light intensity.

[0028] The system of the present invention is further configured with an energy management subsystem. The energy management subsystem executes a dynamic power allocation strategy based on the business load to solve the energy robustness requirements of remote distributed monitoring stations due to the lack of grid support. The internal logic unit of the subsystem monitors the energy status of the battery or photovoltaic panel in real time and performs collaborative optimization on the sleep wake-up frequency of the sensing terminal, the operating intensity of the adaptive surface cleaning unit, and the wireless data backhaul frequency. During periods of stable fluctuation in water quality parameters, the system enters a low-power mode, reducing the self-cleaning frequency and extending the sampling interval. When rapid fluctuations in parameters such as dissolved oxygen or chemical oxygen demand are detected, or when the data authenticity verification algorithm module identifies a trend of biofouling, the energy management subsystem automatically prioritizes the energy input of the adaptive surface purification unit to ensure the physical cleanliness of the sensing front end. This dynamic power adjustment logic achieves optimal allocation of energy resources and significantly improves the system's field patrol cycle.

[0029] The data processing algorithm hub, serving as the core processing brain of the system, is internally equipped with a data authenticity deep verification algorithm module. This module executes the logic for deep verification of the authenticity of the data returned from the perception layer. In ecologically conserving water bodies, the growth of biofilm is a gradual process, and the resulting signal shift is highly concealed. The data authenticity deep verification algorithm module uses the biochemical coupling relationship between parameters such as dissolved oxygen, chemical oxygen demand, total phosphorus, and ammonia nitrogen to construct a correlation evaluation model. Specifically, this module calculates the cross-correlation coefficients and their change trajectories between various biochemical parameters in real time. In one specific algorithm implementation of this invention, the module utilizes the causal relationship between the oxidative degradation process of chemical oxygen demand and dissolved oxygen consumption to calculate the differential coupling degree between the two in the time series. When the system detects that the dissolved oxygen value shows a gradual and continuous decrease, while the chemical oxygen demand index remains constant or fluctuates in the opposite direction due to habitat evolution, the algorithm module executes anomaly discrimination logic. The system identifies that this change does not originate from the evolution of the actual purification process operation state (such as oxygen consumption caused by increased organic load), but rather from the increased resistance to material diffusion caused by the thickening of the biofilm on the sensor probe surface. This module utilizes the gradual and low-frequency characteristics of the zero-point drift caused by biological attachment in the time domain, and establishes a logical mapping of the biofilm growth kinetic equation to score the confidence of the measurement data. If the score is lower than a preset threshold, the algorithm module determines that the sensing layer has experienced source distortion.

[0030] Furthermore, the system of the present invention is equipped with a logic compensation control unit, which receives the judgment result of the data authenticity deep verification algorithm module and executes dynamic correction logic for the closed-loop control command. When the sensor detects sensing distortion caused by biofilm adhesion interference, the logic compensation control unit generates a logic compensation factor. The logic compensation factor performs weighted correction or deconvolution compensation on the original sensing value based on the estimated biofilm thickness and the mass diffusion coefficient. Through this compensation, the system can eliminate the shading effect of the biofilm on the mass exchange interface and restore the true biochemical parameters of the water body. The corrected and accurate data stream is input into the closed-loop control algorithm to guide subsequent automatic aeration adjustment, aquatic plant harvesting cycle planning, or emergency chemical dosing execution instructions. Through this logical compensation mechanism, the system avoids blind intervention caused by data distortion. For example, the system can prevent aeration equipment from performing ineffective high-power aeration due to falsely low readings caused by film formation on the dissolved oxygen sensor probe, thereby achieving energy saving, consumption reduction and refined management.

[0031] The logic compensation control unit also executes global optimization logic for the ecological conservation and purification process. This unit uses real-time biochemical parameters obtained by the multi-parameter monitoring module and combines them with the ecosystem purification efficiency model to predict the decline trend of purification capacity. Through the data after logic compensation, the logic compensation control unit can accurately control the timing of aquatic plant harvesting to prevent over-ripening and death of plants and secondary pollution (i.e., release of endogenous load) caused by decay of remains. In emergency situations, such as when a sudden influx of external sewage is detected, the decision-making instructions generated by the logic compensation control unit have the protective constraints on the fragile dynamic balance of biological populations in the ecological conservation system. By controlling the intensity of physical disturbance and the dosage of reagents, the system can quickly reduce the pollution load while maintaining the stability of the wetland habitat and preventing the collapse of the microbial community due to excessive purification.

[0032] In the specific implementation of this invention, the multi-parameter monitoring module further integrates the logic for tracing the source of sensing failures. When the system determines that a certain parameter has experienced irreversible zero-point drift or physical sensing failure, the module will not simply stop working, but will automatically trigger a sensor health warning signal. Through the data transmission link, the system pushes a diagnostic package containing the root cause of the failure to the maintenance terminal. The diagnostic package details the sources of failure, such as signal saturation caused by irreversible biofilm attachment, potential shift caused by electrochemical corrosion of the probe, or external damage to the mechanical structure (such as blockage of the protective grid). This logic realizes the transformation of the maintenance paradigm from reactive post-maintenance to predictive maintenance, which greatly improves the reliability of the system.

[0033] The system of this invention is also equipped with a habitat stability assessment unit in the processing layer. This unit uses the rate of change of biochemical parameters fed back by the multi-parameter monitoring module to quantitatively assess the impact of the current monitoring intervention on the stability of underwater sediments and the distribution of microbial populations by calculating information entropy or system disturbance index. For example, if the assessment results show that the current automated aeration intensity is too high, which may cause the re-release of heavy metals in the sediment or disrupt the anaerobic / aerobic microenvironment balance, the logic compensation control unit will automatically perform a smoothing process of the intervention intensity. By slowing down the adjustment gradient, it ensures that the improvement of purification efficiency does not come at the expense of habitat stability. This logic ensures that the ecological conservation water body can achieve a steady enhancement of its self-repair capacity under artificial intervention.

[0034] Example The monitoring system of this invention was deployed in a large ecological conservation wetland area.

[0035] Sensing configuration: The multi-parameter monitoring module integrates DO, COD, TP, and NH3-N sensors, and is equipped with an anti-biofouling coating and an adaptive surface purification unit (with a high-frequency vibration frequency set to 30kHz).

[0036] Environmental characteristics: The water body is severely eutrophic, with vigorous algal growth, a large amount of plant debris, and dynamic algal blooms.

[0037] Operating logic: The energy management subsystem adjusts the power of the purification unit according to the DO fluctuation trend; the data authenticity deep verification algorithm module calculates the cross-correlation coefficient between DO and COD in real time; and the logic compensation control unit generates a dynamic correction factor.

[0038] Operating cycle: The system can run continuously for 180 days without requiring manual on-site cleaning of the probe.

[0039] Comparative Example Deploy traditional monitoring systems in the same wetland area.

[0040] Sensing configuration: Standard DO, COD, TP, NH3-N sensors, without self-cleaning function, and using fixed-period sampling.

[0041] Maintenance method: The sensor window needs to be manually cleaned with a brush every 15 days.

[0042] Control logic: The aerator is started and stopped directly based on the original measured values, and it does not have the functions of data authenticity verification and logic compensation.

[0043] Based on the above embodiments and comparative examples, after a 180-day parallel operation test, the following comparative data table was obtained:

[0044] The above comparative data clearly shows that the series of technical effects produced by the present invention have significant certainty. Due to the introduction of a monitoring architecture that deeply integrates multi-dimensional physical coupling and logical compensation, the present invention reduces the data drift rate from 28.5% to 1.8% in the special habitat of ecological conservation water bodies. This improvement is not only an increase in magnitude, but also solves the fundamental problem that sensors cannot work for a long time in highly biologically active environments. By combining a self-balancing sensing terminal structure with an adaptive surface cleaning unit, this invention achieves zero-frequency manual maintenance for up to six months, which has strong engineering application value in the field of distributed ecological monitoring.

[0045] Furthermore, improvements at the logical level, namely the application of a data authenticity deep verification algorithm module, enable the system to identify false measurement values. After the adaptive surface purification unit performs its action, the algorithm module compares the numerical change gradient before and after purification. If the value shows a significant step increase, the system automatically updates the logical compensation factor based on the gradient difference. This closed-loop self-calibration logic ensures that even in extremely harsh habitats with rapid biofilm formation, the monitoring data output by the system can still accurately reflect the biochemical baseline of the ecological conservation water body.

[0046] The present invention provides an integrated intelligent sensor-based ecological conservation and purification precision real-time monitoring system. When processing energy allocation, it also executes a set of priority preemption logic. When the energy management subsystem detects that the energy storage capacity is lower than the critical threshold, the system will prioritize ensuring the minimum sampling frequency of the multi-parameter monitoring module, while temporarily suspending high-power wireless image transmission or high-intensity deep data analysis tasks. However, once the data authenticity deep verification algorithm module issues a risk warning of biofilm formation, the system will trigger a power enhancement command to temporarily increase the power priority of the adaptive surface cleaning unit in order to perform a deep physical descaling action. This energy scheduling logic based on risk priority ensures that the perceived quality of the system does not degrade under extremely low energy conditions.

[0047] In terms of physical connection and signal flow, the various functional modules of this invention exhibit a high degree of integration and synergy. The high-precision signal conditioning unit not only outputs the raw data, but also synchronously outputs the signal-to-noise ratio characteristics and spectral distribution characteristics of the signal. These auxiliary characteristics are input into the data processing algorithm center as an important basis for evaluating the physical state of the sensor (such as probe damage or being completely covered by aquatic plants). When executing the global optimization logic, the logic compensation control unit not only considers water quality parameters, but also combines historical meteorological data and runoff area flow data. By weighting the data from the multi-parameter monitoring module, the system can predict the lowest point of dissolved oxygen in the next 24 hours and initiate pre-aeration accordingly, thereby utilizing the self-regulating inertia of the ecosystem to achieve habitat maintenance at the lowest cost.

[0048] When the system architecture of this invention is implemented, its sensing terminal hardware system can be flexibly expanded according to the specific scale of the ecological conservation area. Data interconnection between each sensing node is achieved through grid networking technology. The data processing algorithm hub can be deployed in the cloud or on local edge computing nodes. On the edge computing nodes, the habitat stability assessment unit performs real-time risk assessment and transmits the accurate data stream after desensitization and compression back to the central platform. This distributed architecture, combined with the self-cleaning and self-compensation logic of this invention, constitutes a three-dimensional monitoring network covering from the microscopic sensing surface to the macroscopic wetland habitat.

[0049] In summary, this invention constructs an intelligent monitoring system that can deeply adapt to the special habitat of ecological conservation water bodies, possesses self-cleaning and self-diagnostic capabilities, and whose control logic has strong physical support. The various technical modules, including the multi-parameter sensing of the sensing terminal hardware system, the structural protection of the self-balancing sensing terminal, the physical descaling of the adaptive surface purification unit, the dynamic power allocation of the energy management subsystem, the in-depth verification of the data processing algorithm center, and the decision correction of the logic compensation control unit, together form a technical closed loop. This solves the technical pain points of sensing distortion, system instability, and blind intervention caused by strong biological activity, and further ensures the long-term stable operation of the ecological conservation and purification process through the logic compensation mechanism.

[0050] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A precise real-time monitoring system for ecological conservation and purification integrating intelligent sensors, characterized in that, include: The sensing terminal hardware system includes a multi-parameter monitoring module that integrates multiple water quality and biochemical parameter sensors. The multi-parameter monitoring module converts the sensing signals into digital feature streams through a high-precision signal conditioning unit. An adaptive surface cleanup unit is integrated with the multi-parameter monitoring module. The adaptive surface cleanup unit uses microscale shear force generated by controlled physical excitation to destroy the bio-attachment physical microenvironment of the sensing interface. The data processing algorithm hub includes a data authenticity deep verification algorithm module. The data authenticity deep verification algorithm module constructs an association evaluation model using the biochemical coupling relationship between different biochemical parameters, and combines the association evaluation model to perform confidence scoring on the digital feature stream. The logic compensation control unit is used to receive the confidence score and generate a logic compensation factor, use the logic compensation factor to dynamically correct the original sensed value, and output the corrected accurate data stream to the closed-loop feedback control link.

2. The ecological conservation and purification precision real-time monitoring system integrating intelligent sensors according to claim 1, characterized in that, The sensing terminal hardware system also includes a self-balancing sensing terminal structure. The self-balancing sensing terminal structure ensures that the sensing window maintains stable directionality of the flow field under test in complex flow fields by configuring a balancing counterweight at the bottom of the multi-parameter monitoring module and adjusting the relative position of the geometric center and the buoyancy center using a hollow structure, combined with a fluid dynamic symmetrical layout. The multi-parameter monitoring module includes a dissolved oxygen sensor, a chemical oxygen demand sensor, a total phosphorus sensor, and an ammonia nitrogen sensor, all fixed within a modular housing.

3. The ecological conservation and purification precision real-time monitoring system integrating intelligent sensors according to claim 2, characterized in that, The sensing terminal hardware system is externally configured with a protective grid, and the grid bars of the protective grid are designed with a non-uniform distribution. In the direction of water flow ingress, the gaps between the grid bars are widened to improve the water flow field exchange rate. In the lateral direction and in the direction susceptible to impact from emergent plant debris, the gaps between the grid bars are designed to prevent physical collision interference, and the protective grids are used to rectify the flow of fluid to create a laminar flow environment at the front end of the sensing window.

4. The ecological conservation and purification precision real-time monitoring system integrating intelligent sensors according to claim 1, characterized in that, The conversion logic executed by the high-precision signal conditioning unit includes: The charge fluctuation, photocurrent intensity, or potential difference signals captured by the multi-parameter monitoring module are captured in real time using an operational amplifier and a gain controller. By setting the cutoff frequency and performing low-pass filtering, physical noise generated by suspended particulate matter impacting the sensing surface and high-frequency electromagnetic interference from the environment are filtered out. Dynamic gain compensation is performed, and the quantization resolution is automatically adjusted according to the range of the input signal to maintain the linearity of the original sensing data across the entire range.

5. The ecological conservation and purification precision real-time monitoring system integrating intelligent sensors according to claim 1, characterized in that, The adaptive surface cleaning unit includes a high-frequency vibration module that performs resonant excitation and a directional fluid flushing module that performs directional fluid flushing. The high-frequency vibration module uses a piezoelectric ceramic transducer to perform controlled vibration, thereby disrupting the adhesion bond energy of the biofilm at the sensing interface through cavitation effect; The directional fluid flushing module uses a micro submersible pump array and nozzles to generate local jets, expelling the detached biological attachments from the material exchange interface microenvironment.

6. The ecological conservation and purification precision real-time monitoring system integrating intelligent sensors according to claim 5, characterized in that, The adaptive surface purification unit has adaptive adjustment logic based on environmental characteristics. The logic compensation control unit dynamically adjusts the vibration frequency of the high-frequency vibration module and the jet intensity of the directional fluid flushing module according to the changes in water temperature and light intensity. The triggering conditions for the adaptive surface cleaning unit include: The data authenticity deep verification algorithm module identifies biological attachment trends, system operation reaching a preset maintenance cycle, or abnormal attenuation of sensing values ​​that conform to biofilm characteristics.

7. The ecological conservation and purification precision real-time monitoring system integrating intelligent sensors according to claim 1, characterized in that, It also includes an energy management subsystem that executes a dynamic power allocation strategy based on service load; The energy management subsystem includes dynamic power adjustment logic, which monitors the battery's energy status in real time and performs priority preemption. During periods of stable water quality, the energy management subsystem reduces the system sampling frequency and enters a dormant mode. When the data processing algorithm detects a risk of biofouling or a sudden change in parameters, the energy management subsystem automatically increases the energy input priority of the adaptive surface purification unit and the multi-parameter monitoring module to ensure the physical cleanliness of the sensing front end and the continuity of data.

8. The ecological conservation and purification precision real-time monitoring system integrating intelligent sensors according to claim 1, characterized in that, The data authenticity deep verification algorithm module executes the following authenticity deep verification logic: Real-time acquisition of change curves for dissolved oxygen, chemical oxygen demand, total phosphorus, and ammonia nitrogen parameters; Establish a differential coupling model among the parameters and calculate the causal relationship between the degradation rate of chemical oxygen demand and the dissolved oxygen consumption rate. When the dissolved oxygen value shows a gradual and continuous decrease, and the differential coupling model shows that the chemical oxygen demand index remains constant or fluctuates in the opposite direction, the data authenticity deep verification algorithm module determines that a zero-point drift caused by the biofilm shielding effect has occurred. A logical mapping of the biofilm growth kinetic equation is established. Combined with the numerical gradient difference before and after the execution of the adaptive surface purification unit, the confidence score of the current sensing data is calculated, and the sensing failure tracing logic is extracted to distinguish the root cause of sensing distortion as irreversible biofilm formation, electrochemical corrosion, or structural damage.

9. A precise real-time monitoring system for ecological conservation and purification integrating intelligent sensors as described in claim 8, characterized in that, The dynamic correction logic executed by the logic compensation control unit includes: The logical compensation factor is calculated based on the membrane thickness estimate derived from the biofilm growth kinetic equation and the mass diffusion coefficient. The original sensed values ​​are deconvolutionally compensated or weighted by the logical compensation factor to eliminate the interference of diffusion resistance of biofilm on the material exchange interface and restore the true biochemical data of the water body. The corrected precise data stream is input into the closed-loop control algorithm to accurately control the operating parameters of automated aeration regulating equipment, aquatic plant harvesting actuators, or emergency pesticide dosing devices, thereby avoiding invalid actuator actions caused by inaccurate data.

10. The ecological conservation and purification precision real-time monitoring system integrating intelligent sensors according to claim 1, characterized in that, The data processing algorithm hub is also equipped with a habitat stability assessment unit. The habitat stability assessment unit uses the rate of change of biochemical parameters fed back by the multi-parameter monitoring module to calculate information entropy or system disturbance index, and assesses the impact of the current monitoring intervention on the stability of underwater sediments and the distribution of microbial populations. If the evaluation results show that the intervention intensity of the closed-loop feedback control link exceeds the habitat carrying capacity threshold, the logic compensation control unit automatically performs smoothing processing of the intervention intensity to maintain the habitat stability of the ecological conservation water body by slowing down the adjustment gradient.

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