Physicochemical parameter detection system and method applied to constructed wetland

Abstract

This invention belongs to the field of constructed wetland detection, specifically a physicochemical parameter detection system and method for constructed wetlands. The system includes a global sensing and acquisition module, a multi-source time-series normalization and integration module, an interface parameter quantification and deduction module, an efficiency level assessment module, a fault cause tracing and decision-making module, and a global data collaborative management and control module. This invention collects physicochemical parameters synchronously in a distributed manner, performs normalization processing such as time-series calibration and dimensional unification, quantifies and deduces matrix blockage, interface ion migration, and microbial biochemical intensity, calculates the influent load matching degree and purification efficiency attenuation index, classifies and assesses the operational status, accurately traces fault causes, and generates operation and maintenance plans. This significantly improves detection accuracy and the level of intelligent management, which is beneficial for ensuring stable purification efficiency in ecological management areas and reducing the management difficulty of ecological management areas.

Description

Technical Field

[0001] This invention relates to the field of constructed wetland testing technology, specifically to a system and method for testing the physicochemical parameters of constructed wetlands. Background Technology

[0002] Constructed wetlands, as the core component of ecological water treatment, directly determine the water treatment effect and long-term stable operation based on the accuracy of physicochemical parameter monitoring, the level of quantitative analysis of interfacial processes, the scientific nature of purification efficiency assessment, and the effectiveness of fault tracing. With the intelligent upgrading of water body ecological remediation and operation and maintenance, traditional ecological management zone detection and control models can no longer meet actual needs. The level of intelligence and refinement of conventional detection and operation control technologies is significantly insufficient, and the following defects still exist in practical applications: First, it is impossible to quantify and extrapolate core interface parameters such as matrix blockage, interfacial ion migration rate, and microbial biochemical reaction intensity, making it difficult to accurately analyze the microscopic situation. Secondly, the system fails to quantify and classify the operational status through the matching degree of influent load and the purification efficiency decay index. The efficiency assessment lacks objective standards and cannot cross-verify the causes of the problem with multiple parameters or automatically generate operation and maintenance plans. This makes it difficult to support refined, intelligent, and long-term operation and maintenance, and it cannot stably guarantee purification efficiency or reduce management difficulty.

[0003] Therefore, developing a solution for detecting the geochemical parameters of artificial wetlands that can achieve quantitative deduction of interface parameters, hierarchical assessment of purification efficiency, precise tracing of fault causes, and collaborative management of data across the entire area has become an urgent problem to be solved in the field of artificial wetland detection. Summary of the Invention

[0004] The purpose of this invention is to provide a physicochemical parameter detection system and method for constructed wetlands, so as to solve the technical defects mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a physicochemical parameter detection system for constructed wetlands, comprising a global sensing and acquisition module, a multi-source time-series normalization and integration module, an interface parameter quantification and deduction module, an efficiency level judgment module, a fault cause tracing and decision-making module, and a global data collaborative management and control module; The global sensing and acquisition module acquires the artificial wetlands that need to be monitored and marks them as ecological management areas. It is used for the distributed synchronous acquisition of physical and chemical parameters of the entire ecological management area and the removal of abnormal data. The multi-source time series normalization and integration module is used for the time series calibration, dimensional unification and missing data completion of multi-source heterogeneous data. The interface parameter quantification and deduction module is used for the quantitative analysis of matrix blockage, interface ion migration and microbial biochemical intensity. The efficiency level assessment module is used to calculate the influent load matching degree and purification efficiency attenuation index and classify the operation status. The fault cause tracing decision module is used to accurately locate the fault cause and generate operation and maintenance plan. The full-domain data collaborative management and control module is used for full-process data storage, visualization display and linkage control.

[0006] Furthermore, the global sensing and acquisition module collects physicochemical parameters of the ecological management area, such as matrix porosity, dissolved oxygen, pH, redox potential, water temperature, influent flow rate, total phosphorus concentration, and ammonia nitrogen concentration, by deploying a sensor array. It also has built-in data noise reduction logic to remove interference and outliers, performs real-time self-checks of equipment operation and marks fault timing, and transmits valid data via wired or wireless protocols after adding high-precision timestamps.

[0007] Furthermore, the multi-source time series normalization and integration module uses unified time message calibration data timestamps to complete the unified conversion of analog and digital quantities, uses the mean interpolation of adjacent effective values ​​to fill in short-term interrupted missing data, and the normalized standardized dataset is transmitted to the interface parameter quantization and deduction module and the global data collaborative management and control module.

[0008] Furthermore, the interface parameter quantification and deduction module judges the matrix clogging status by the ratio of real-time matrix porosity to the initial baseline porosity, quantifies the interface ion migration rate by combining pH and redox potential, and deduces the intensity of microbial biochemical reactions by combining dissolved oxygen and water temperature, thus integrating them into an integrated interface analysis dataset.

[0009] Furthermore, the efficiency level assessment module calculates the influent load matching degree based on real-time influent flow, pollutant concentration and design baseline parameters, and classifies the load into three states: overload, underload and load matching.

[0010] Furthermore, the efficiency level assessment module combines matrix structure, biochemical intensity, load deviation, and output compliance rate to calculate the purification efficiency attenuation index.

[0011] Furthermore, after calculating the purification efficiency attenuation index, the operation status of the ecological management area is divided into three levels: healthy, slightly attenuated, and severely attenuated, based on the purification efficiency attenuation index, thereby quantitatively determining the overall purification performance of the ecological management area.

[0012] Furthermore, the fault cause tracing and decision-making module accurately locates core faults such as matrix blockage, insufficient microbial activity, and overloaded water inlet through multi-parameter cross-verification, and automatically generates corresponding operation and maintenance rectification plans.

[0013] Furthermore, the full-domain data collaborative management and control module establishes a dual backup mechanism for the entire process data, enabling long-term traceability and retrieval of data. It also visualizes information such as monitoring data, analysis results, fault diagnosis, and operation and maintenance plans, and issues collaborative control commands for adjusting collection parameters, starting and stopping equipment, and regulating valves.

[0014] This invention also proposes a method for detecting physicochemical parameters in constructed wetlands, comprising the following steps: Step 1: Distributed sensing of physical and chemical parameters across the entire domain; Step 2: Normalization and integration of multi-source time-series data; Step 3: Quantitative deduction of interface parameters; Step 4: Quantitative analysis of performance levels; Step 5: Traceability of fault causes and operation and maintenance decisions; Step 6: Collaborative management and coordinated control of data across the entire domain.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In this invention, by synchronously collecting physicochemical parameters of the ecological management area across the entire region and completing the normalization processing of multi-source data, the matrix blockage situation, interface ion migration rate and microbial biochemical reaction intensity are accurately quantified and deduced. At the same time, data archiving and global collaborative management are completed, improving the integrity, consistency and micro-environmental analysis accuracy of the detection data, and providing a reliable data foundation for judging the operation status of the ecological management area.

[0016] 2. In this invention, by calculating the water inflow load matching degree and the purification efficiency attenuation index, the operation status of the ecological management area is quantitatively graded. Combined with multi-parameter cross-verification, the causes of failures are accurately traced, and an actionable operation and maintenance rectification plan is automatically generated. This provides reliable quantitative support for the performance evaluation, hidden danger investigation and scientific regulation of the ecological management area, and ensures the stable operation of the purification efficiency of the ecological management area. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a schematic diagram of the overall system structure of the present invention; Figure 2 This is a schematic diagram of the operation method of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: Refer to Figure 1As shown, the physicochemical parameter detection system for constructed wetlands proposed in this invention includes a global sensing and acquisition module, a multi-source time-series normalization and integration module, an interface parameter quantification and deduction module, an efficiency level assessment module, and a global data collaborative management and control module. The global sensing and acquisition module, as the front-end sensing terminal of the system, acquires the artificial wetlands that need to be monitored and marks them as ecological management areas. It realizes the distributed synchronous acquisition of physical and chemical parameters of the entire ecological management area, completes the preliminary screening and standardized transmission of raw data, and provides a continuous, stable and reliable data source for subsequent data processing.

[0020] Specifically, the all-domain perception and acquisition module is equipped with multiple sets of monitoring sensor arrays to continuously collect key physicochemical parameters, including matrix porosity, dissolved oxygen, water pH, redox potential, water temperature, influent flow rate, total phosphorus concentration, and ammonia nitrogen concentration. It also has built-in data noise reduction logic to automatically remove abrupt abnormal values ​​caused by wind and wave impacts and electromagnetic interference. It also performs real-time self-checks of the equipment's power supply voltage, transmission rate, and online operating conditions. When problems such as power outages, disconnections, or data freezes occur, it automatically marks the timing and anomaly tags. All filtered valid data are uniformly annotated with high-precision timestamps and transmitted externally via wired or wireless communication protocols. This provides a continuous, stable, and reliable source of data for the multi-source time-series normalization and integration module and the global data collaborative management and control module, ensuring the continuity and integrity of parameter acquisition across the entire system.

[0021] The multi-source time series normalization and integration module receives multi-source heterogeneous data collected by the global perception acquisition module, and completes time series calibration, unit unification, missing data completion and format normalization processing to eliminate problems such as time series misalignment, unit confusion and data gaps in the data acquisition process, and provides a standardized and highly consistent integrated dataset for subsequent quantitative analysis.

[0022] Specifically, the multi-source time-series normalization and integration module receives multi-source heterogeneous data transmitted by the global perception acquisition module, completes time-series calibration, dimension unification, missing data completion and format regularization, and calibrates all data timestamps based on the unified time message to eliminate time-series misalignment errors of multiple devices and ensure that parameters match consistently at the same cross-section at the same moment. To address the issues of mixed analog and digital quantities and inconsistent units, standardized conversion and adaptation were completed. To fill data gaps caused by short-term transmission interruptions, interpolation using the mean of adjacent valid values ​​was employed to maintain the integrity of the time-series chain. The standardized dataset was transmitted in two paths: one path was connected in real-time to the interface parameter quantification and deduction module as the basis for quantitative calculations, and the other path was synchronously archived and stored in the global data collaborative management module, enabling traceable retention of original monitoring data and providing basic support for long-term operation and maintenance analysis.

[0023] The interface parameter quantification and deduction module relies on the measured parameters directly detected on-site to conduct deductions around matrix blockage evolution, interface ion migration, and the intensity of microbial biochemical reactions, thereby achieving precise quantitative analysis of the microenvironment of the ecological management area and fully analyzing the quality of the micro-operating environment of the ecological management area.

[0024] Specifically, after receiving the standardized dataset, the interface parameter quantification and deduction module first retrieves the real-time measured value of matrix porosity K, and combines it with the baseline porosity Ko of the ecological management area at the initial stage of operation stored in the system. It directly judges the degree of matrix porosity shrinkage and the accumulation of silt by comparing the values. Based on the ratio of the two sets of values ​​(K / Ko), it clearly reflects the structural deterioration caused by the compaction of matrix filler and the accumulation of debris. For example, when the ratio of K to Ko continues to decrease, it is directly determined that there is debris accumulation, biofilm accumulation or filler compaction problem inside the matrix, and the permeability of the matrix gradually weakens.

[0025] Furthermore, based on the comparison of matrix structure, combined with the detection items of water acid and alkali and the redox potential parameters, the ion exchange and diffusion capacity of pollutants at the water-matrix interface is quantified, and the ion migration rate calculation formula is embedded in real time. The ion migration rate calculation formula is: V=b×Ec×|pH-pHo|. Wherein, V: interfacial ion migration rate, which is calculated in real time by the formula and characterizes the diffusion and migration ability of pollutants at the water-matrix interface; b: migration correction weight value, preferably in the range of 0.001 to 0.01, which is calibrated and solidified by on-site test based on the characteristics of pollutant components entering the water in the ecological management area. E: Real-time oxidation-reduction potential, continuously collected by relevant sensors in the global sensing acquisition module, and averaged every 5 minutes; c: Acid-base correction weight value, preferably ranging from 0.5 to 2.0, determined in conjunction with environmental tolerance tests of native functional microorganisms in the ecological management area; pH: Real-time water acidity and alkalinity, continuously collected by an online pH sensor; pHo: Optimal pH benchmark for microorganisms, a fixed constant, usually taken as 7.5, which can be adjusted according to the type of ecological management area.

[0026] Once the interfacial ion migration rate (V) is calculated, continuous judgment is immediately made based on the range of V values. When V is within a reasonable range, it is determined that the exchange of substances at the water matrix interface is unimpeded and the migration and transformation of pollutants are not restricted by the environment. When V is consistently lower than the lower limit of the normal range, it is determined that acid-base imbalance or abnormal redox environment inhibits interfacial substance exchange and weakens the basic conditions for micro-purification. When V is abnormally high, it is determined that the ion disorder in the water body is aggravated, which is prone to causing scaling and sludge accumulation on the matrix surface.

[0027] Simultaneously, by combining measured parameters of dissolved oxygen in the water and ambient temperature, a quantitative extrapolation calculation of the metabolic activity of interfacial microorganisms was completed. The calculation formula is: I=d×D×T; Among them, I represents the intensity of interfacial biochemical reaction, which directly reflects the comprehensive ability of microorganisms in the ecological management area to degrade and metabolize pollutants; d represents the community adaptation correction weight value, which is preset and calibrated according to the type of vegetation planted in the ecological management area and the structure of the native microbial community; D represents the real-time dissolved oxygen concentration, which is continuously measured and collected by a dedicated sensing device; and T represents the real-time water environment temperature, which is directly collected by a temperature sensor.

[0028] After calculating the intensity of the interfacial biochemical reaction, the situation is further assessed. A higher I value indicates stronger overall metabolic activity of the microorganisms and more stable biochemical purification efficiency in the ecological management area. If I continuously declines in stages, it is directly determined that factors such as insufficient dissolved oxygen supply and abnormal water temperature fluctuations have caused a decline in microbial activity. If I fluctuates significantly and irregularly, it is determined that the micro-ecological environment of the area is in an unstable state.

[0029] Finally, the matrix structure comparison results, ion migration rate determination conclusions, and biochemical reaction intensity assessment data are integrated into an integrated interface analysis dataset and fully transmitted to the global data collaborative management module.

[0030] The global data collaborative management and control module serves as the central hub of the system, receiving all data transmitted from each front-end module, classifying and archiving it, and establishing a dual backup mechanism to ensure long-term traceability and retrieval of data, as well as its visualization, making it easier for managers to intuitively grasp the status of the ecological management area.

[0031] In addition, the whole-domain data collaborative management and control module can also issue collaborative control commands based on the ecological management area improvement plan, realize linkage operations such as adjusting the collected parameters, starting and stopping the aeration equipment, and adjusting the water inlet valve, and support the refined, intelligent and long-term management and control of the ecological management area.

[0032] Example 2: Refer to Figure 1 As shown, the difference between this embodiment and Embodiment 1 is that it also includes an efficiency level assessment module. The interface parameter quantification and deduction module completely transmits the integrated interface analysis dataset, which includes matrix structure comparison results, ion migration rate determination conclusions, and biochemical reaction intensity assessment data, to the efficiency level assessment module, providing microscopic data support for macroscopic purification efficiency assessment. The efficiency level assessment module successively completed two levels of quantitative calculations: water influent load matching degree and purification efficiency attenuation index. Based on the calculation results, it divided the operation level into clear levels and carried out continuous operation condition assessment with numerical results as the core throughout the process. This enabled the quantitative classification and determination of the overall purification performance of the ecological management area, providing a basis for hazard tracing and control decisions.

[0033] Specifically, the efficiency level assessment module receives the integrated interface analysis dataset transmitted by the interface parameter quantification and deduction module, and simultaneously accesses the measured parameters such as water inflow and concentration of various pollutants in the standardized dataset. First, it performs quantitative calculation of the carrying capacity of the ecological management area and verifies the degree of matching between the water inflow load and the design conditions. The calculation formula is: M=[Q×(C1+C2+C3)] / [Qo×(C1o+C2o+C3o)]; Where M: load matching degree, which represents the ratio of actual inflow load to design load; Q: Real-time water inflow rate is continuously collected by an electromagnetic flow meter with an accuracy of ±0.5%; C1: Real-time COD concentration, measured by an online COD analyzer; C2: Real-time ammonia nitrogen concentration, measured by an online ammonia nitrogen sensor; C3: Real-time total phosphorus concentration, measured by an online total phosphorus sensor. Qo: Design rated flow rate, engineering set value is pre-stored in the system; C1o: Design baseline COD concentration, a fixed baseline parameter during the engineering design phase, pre-stored in the system; C2o: Design baseline ammonia nitrogen concentration, a fixed baseline parameter during the engineering design phase, pre-stored in the system; C3o: Design baseline total phosphorus concentration, a fixed baseline parameter during the engineering design phase, pre-stored in the system.

[0034] Once the load matching degree M is calculated, the operating condition adaptation division can be carried out, as shown below: When the M value is greater than 1.5, it is directly determined that the ecological management area has been in a state of long-term water inflow overload, and the amount of pollution input exceeds the ecological carrying capacity limit. When the M value is less than 0.5, it is determined that the water inflow load is insufficient. Long-term low-load operation of the ecosystem in the ecological management area is prone to cause degradation of the microbial community. When M is in the range of 0.5 to 1.5, it is determined that the influent load is well adapted and the external input conditions meet the requirements for stable operation of the ecological management area.

[0035] Furthermore, based on the assessment of the influent load, and combined with the proportion of substrate structure loss, the magnitude of microbial biochemical intensity decline, the degree of load deviation, and the stability of the output water quality, a purification efficiency attenuation index is obtained through multi-dimensional linear weighted calculation. This fully quantifies the degree of attenuation of the overall purification capacity of the ecological management area. The calculation formula is as follows: S=μ×(1-K / Ko)+e×(1-I / Io)+ω×∣M-1∣+ρ×(1-R); Wherein, S: purification efficiency decay index, the larger the value, the more serious the weakening of purification ability, the preferred range is 0 to 1; K: Real-time measured matrix porosity; Ko: Initial reference porosity, which is a fixed reference value calibrated by multiple measurements before the system is put into operation; I: Real-time biochemical reaction intensity, calculated by the interface parameter quantification and deduction module; Io: Steady biochemical intensity of stable operation in the ecological management area, taken from the long-term average of the first 30 days of stable operation of the system. R: Output water quality compliance rate, with a value of 0 to 1, is calculated from the output monitoring data within a nearby fixed time period. The calculation method is: number of times the output meets the standard / total number of tests. μ, e, ω, ρ: Preset fixed weight values, the sum of the four is 1, usually μ=0.3, e=0.3, ω=0.2, ρ=0.2, which can be adjusted according to the type of ecological management zone.

[0036] After calculating the purification efficiency attenuation index S, a hierarchical classification and comprehensive assessment are performed to form a standardized judgment logic. The judgment strategy can be referenced as follows: When 0≤S<0.2, the overall operation of the ecological management area is judged to be healthy, the matrix structure, microbial activity and water inflow load are all within a reasonable range, and the purification efficiency is stable and meets the standards. When 0.2≤S<0.4, the ecological management area is judged to have entered a state of mild decline, with one or more test items showing slight deterioration, the purification performance showing a slow downward trend, and there is a potential risk of gradual deterioration. When S≥0.4, the ecological management area is judged to be in severe degradation operation, with multiple core parameters deteriorating simultaneously, the overall purification function significantly reduced, and the output cannot be guaranteed to be stable and qualified in the long term.

[0037] Finally, the load matching conclusions, performance degradation levels, and other information are compiled and transmitted synchronously to the full-domain data collaborative management module to provide quantitative basis for hazard tracing.

[0038] Example 3: Refer to Figure 1 As shown, the difference between this embodiment and Embodiments 1 and 2 is that it also includes a fault cause tracing and decision-making module. This module identifies the core fault type and root cause based on the operational analysis information from the performance hierarchy assessment module and the performance hierarchy assessment module. For example: When S≥0.4 and K / Ko<0.7, the core problem can be determined to be blockage of the matrix filler. When S≥0.4 and I / Io<0.6, the core problem can be determined to be insufficient activity of interfacial microorganisms. When S≥0.4 and M>0.5, it can be determined that the influent pollution load is overloaded.

[0039] By cross-referencing multiple parameters, the problem of misjudgment due to a single detection item is effectively avoided, and a comprehensive diagnostic result including load status, attenuation index and fault type is formed.

[0040] Furthermore, based on the fault diagnosis results, combined with the type, scale, and operating conditions of the ecological management area, the system can automatically match and generate directly implementable operation and maintenance rectification plans and control strategies to improve the intelligent management and control level of the ecological management area. Examples of operation and maintenance rectification plans and control strategies are as follows: When the substrate filler is clogged, it is recommended to aerate and clear the blockage or replace the filler in some areas; when the activity of interfacial microorganisms is insufficient, it is recommended to supplement with microbial agents or optimize the dissolved oxygen concentration in the influent; when the influent pollution load is overloaded, it is recommended to reduce the influent flow rate or increase the reflux ratio.

[0041] Example 4: Refer to Figure 2 As shown, the difference between this embodiment and Embodiments 1, 2, and 3 is that it proposes a method for detecting physicochemical parameters applied to constructed wetlands, including the following steps: Step 1: Distributed sensing of global physicochemical parameters: Multiple sets of monitoring sensor arrays are deployed to collect key physicochemical parameters such as matrix porosity, dissolved oxygen, water pH, redox potential, water temperature, influent flow rate, total phosphorus concentration, and ammonia nitrogen concentration in a distributed and synchronous manner. The built-in noise reduction logic eliminates abnormal fluctuations caused by wind, waves, and electromagnetic interference. The sensor power supply, transmission, and online operating conditions are checked in real time, and faulty equipment is marked with time sequence and abnormal tags. The effective data is annotated with high-precision timestamps and transmitted to subsequent modules via wired / wireless protocols.

[0042] Step 2: Normalization and integration of multi-source time series data: It receives multi-source heterogeneous data collected from the entire domain perception system, calibrates all data timestamps with unified message delivery, and eliminates timing misalignment errors among multiple devices; it unifies the units and data formats of analog and digital quantities, and uses interpolation of the mean of adjacent effective values ​​to fill data gaps caused by short-term transmission interruptions; it generates a standardized integrated dataset, which is transmitted to the interface parameter quantization and deduction module on one side and archived to the entire domain data collaborative management and control module on the other.

[0043] Step 3: Quantitative Deduction of Interface Parameters: The real-time matrix porosity and the initial baseline porosity are retrieved, and the matrix clogging and compaction status is judged by numerical comparison. The interfacial ion migration rate is calculated by combining the water pH and redox potential to determine the smoothness of pollutant exchange at the water-matrix interface. The intensity of interfacial biochemical reaction is calculated based on dissolved oxygen and ambient temperature to infer the microbial metabolic activity and purification capacity. The matrix status, ion migration, and biochemical intensity results are integrated into an integrated interface analysis dataset.

[0044] Step 4: Quantitative Assessment of Efficiency Levels By accessing measured data on influent flow and pollutant concentration, the influent load matching degree is calculated to determine whether the ecological management area is in a state of overload, underload, or fit. Based on matrix structure loss, biochemical intensity decay, load deviation, and output compliance rate, the purification efficiency decay index is calculated. According to the decay index, the operation status of the ecological management area is divided into three levels: healthy, slightly decayed, and severely decayed.

[0045] Step 5: Trace the root cause of the failure and make operational decisions: By combining data on purification efficiency attenuation index, matrix porosity ratio, biochemical intensity ratio, and load matching degree, the core causes of failures are accurately located (matrix packing blockage, insufficient microbial activity, and overload of influent pollution load). Based on the type of failure and the operating conditions of the ecological management area, standardized operation and maintenance rectification plans are automatically generated, including aeration and unblocking, partial replacement of packing materials, supplementation of microbial agents, optimization of dissolved oxygen, and adjustment of influent flow rate / return ratio.

[0046] Step Six: Comprehensive Data Collaborative Management and Interconnected Control: The full-domain data collaborative management and control module receives data from the entire process, completes classification, archiving and dual backup, and achieves long-term data traceability; it visualizes and displays collected data, simulation results, performance assessment, fault diagnosis and operation and maintenance solutions; and it issues collaborative control commands such as adjusting collection parameters, starting and stopping aeration equipment and adjusting water inlet valves according to the management and control needs of the ecological management area, so as to achieve refined, intelligent and long-term management and control of the ecological management area.

[0047] The working principle of this invention is as follows: During use, a distributed sensor array collects physicochemical parameters of the ecological management area across the entire area. After time-series calibration, dimensional unification, and missing data completion, the data is normalized. Furthermore, the invention quantifies and extrapolates matrix blockage, interfacial ion migration, and microbial biochemical intensity, calculates the influent load matching degree and purification efficiency attenuation index, and classifies and analyzes the operational status. It accurately traces the causes of faults and generates operation and maintenance plans to solve the problems of insufficient micro-quantification, lack of operational status classification, and difficulty in tracing faults in traditional methods. This enables reasonable quantitative judgment of the operational status of the ecological management area and accurate fault location, supports intelligent operation and maintenance and coordinated control, helps ensure the stability of purification efficiency in the ecological management area, and significantly reduces the management difficulty of the ecological management area.

[0048] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A physicochemical parameter detection system for constructed wetlands, characterized in that, It includes a full-domain perception and acquisition module, a multi-source time-series normalization and integration module, an interface parameter quantification and deduction module, an efficiency level analysis module, a fault cause tracing and decision-making module, and a full-domain data collaborative management and control module; The global sensing and acquisition module acquires the artificial wetlands that need to be monitored and marks them as ecological management areas. It is used for the distributed synchronous acquisition of physical and chemical parameters of the entire ecological management area and the removal of abnormal data. The multi-source time series normalization and integration module is used for the time series calibration, dimensional unification and missing data completion of multi-source heterogeneous data. The interface parameter quantification and deduction module is used for the quantitative analysis of matrix blockage, interface ion migration and microbial biochemical intensity. The efficiency level assessment module is used to calculate the influent load matching degree and purification efficiency attenuation index and classify the operation status. The fault cause tracing decision module is used to accurately locate the fault cause and generate operation and maintenance plan. The full-domain data collaborative management and control module is used for full-process data storage, visualization display and linkage control.

2. The physicochemical parameter detection system for constructed wetlands according to claim 1, characterized in that, The global perception and acquisition module collects several physical and chemical parameters of the ecological management area by deploying a sensor array. It also has built-in data noise reduction logic to remove interference and outliers, performs real-time self-checks of equipment operation and marks fault timing, and transmits the valid data via wired or wireless protocols after adding a high-precision timestamp.

3. The physicochemical parameter detection system for constructed wetlands according to claim 1, characterized in that, The multi-source timing normalization and integration module calibrates the data timestamp with unified time transmission messages, completes the unified conversion of analog and digital quantities, and uses the mean interpolation of adjacent effective values ​​to fill in short-term interrupted missing data.

4. The physicochemical parameter detection system for constructed wetlands according to claim 1, characterized in that, The interface parameter quantification and extrapolation module judges the matrix clogging status by the ratio of real-time matrix porosity to the initial baseline porosity, quantifies the interface ion migration rate by combining pH and redox potential, and extrapolates the intensity of microbial biochemical reactions by combining dissolved oxygen and water temperature, thus integrating them into an integrated interface analysis dataset.

5. The physicochemical parameter detection system for constructed wetlands according to claim 1, characterized in that, The efficiency level assessment module calculates the influent load matching degree based on real-time influent flow, pollutant concentration and design baseline parameters, and classifies the load into three states: overload, underload and load matching.

6. The physicochemical parameter detection system for constructed wetlands according to claim 5, characterized in that, The efficiency level assessment module combines matrix structure, biochemical intensity, load deviation and output compliance rate to calculate the purification efficiency attenuation index.

7. The physicochemical parameter detection system for constructed wetlands according to claim 6, characterized in that, After calculating the purification efficiency attenuation index, the operation status of the ecological management area is divided into three levels: healthy, slightly attenuated, and severely attenuated, based on the purification efficiency attenuation index, so as to quantitatively determine the overall purification performance of the ecological management area.

8. The physicochemical parameter detection system for constructed wetlands according to claim 1, characterized in that, The fault cause tracing and decision-making module accurately locates core faults, including matrix blockage, insufficient microbial activity, and overload of water inlet, through cross-verification of multiple parameters, and automatically generates corresponding operation and maintenance rectification plans.

9. The physicochemical parameter detection system for constructed wetlands according to claim 1, characterized in that, The full-domain data collaborative management and control module establishes a dual backup mechanism for all-process data, and visualizes monitoring data, analysis results, fault diagnosis and operation and maintenance solutions, and issues collaborative control commands for adjusting collection parameters, starting and stopping equipment and regulating valves.

10. A method for detecting physicochemical parameters in constructed wetlands, comprising the physicochemical parameter detection system for constructed wetlands as described in any one of claims 1-9, characterized in that, Includes the following steps: Step 1: Distributed sensing of physical and chemical parameters across the entire domain; Step 2: Normalization and integration of multi-source time-series data; Step 3: Quantitative deduction of interface parameters; Step 4: Quantitative analysis of performance levels; Step 5: Traceability of fault causes and operation and maintenance decisions; Step 6: Collaborative management and coordinated control of data across the entire domain.

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