A sewage treatment process model parameter correction method and system

Abstract

The present invention relates to the field of sewage treatment technology, and in particular to a method and system for correcting parameters of a sewage treatment process model. The method comprises the following steps: collecting sensor data from multiple facilities through a sewage treatment sensor network, extracting the topological structure of the sewage pipe, projecting the data into the topological structure and marking the location information, constructing an initial sewage treatment process model based on this information, and extracting the sewage data set of the treatment process. The system simulates the sewage treatment environment, generates virtual flow field data, tracks particle motion, draws sedimentation curves and generates particle sedimentation trajectory offset data, obtains sedimentation tank morphological parameters, reconstructs the sedimentation tank architecture, predicts sedimentation imbalance status, and analyzes model parameters through abnormal state backtracking, performs corrections, and generates an optimized sewage treatment process model. The present invention achieves an overall improvement in the operating efficiency and treatment effect of sewage treatment facilities, and promotes the intelligent and refined development of sewage treatment technology.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to a sewage treatment process model parameter correction method and system. Background Art

[0002] Existing wastewater treatment technologies often rely on empirical experience and static models, resulting in insufficient response to dynamic changes in the wastewater treatment process. Traditional monitoring methods cannot reflect the wastewater treatment status in real time, resulting in low treatment efficiency and waste of resources. Especially in complex wastewater treatment environments, they cannot accurately capture changes in wastewater characteristics, limiting the improvement of treatment effectiveness. Many wastewater treatment facilities face various unforeseen situations during operation, such as water quality fluctuations and flow rate changes. These factors have a direct impact on treatment effectiveness. Existing technologies are unable to cope with these changes, lacking flexibility and adaptability, and are unable to achieve rapid adjustments. This leads to increased operating costs and unstable treatment results, which in turn affects environmental protection and resource reuse. Traditional wastewater treatment facilities face problems such as uneven particle settling and sedimentation imbalance. Existing technologies are relatively slow to identify and respond to these problems, failing to achieve effective dynamic adjustment and optimization. Traditional methods generally rely on regular manual monitoring and adjustments, resulting in slow response speed and unstable treatment results during the treatment process. The lack of scientific sedimentation analysis and flow field simulation makes it difficult to provide real-time data support for operational decisions. Summary of the Invention

[0003] Based on this, it is necessary to provide a sewage treatment process model parameter correction method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for calibrating parameters of a sewage treatment process model is provided, comprising the following steps:

[0005] Step S1: collecting sensor data of multiple sewage treatment facilities through a sewage treatment sensor network; extracting the sewage pipe topology of the sewage treatment sensor network; projecting the sensor data of multiple sewage facilities into the sewage pipe topology and marking the corresponding location information;

[0006] Step S2: constructing an initial sewage treatment process model based on corresponding location information and multi-point sewage facility sensor data; extracting a treatment process sewage data set in the initial sewage treatment process model;

[0007] Step S3: performing sewage treatment environment simulation based on the sewage treatment process data set to generate a simulated sewage treatment process environment; simulating virtual flow field data based on the simulated sewage treatment process environment and the sewage treatment process data set;

[0008] Step S4: Tracking particle motion based on the wastewater dataset and virtual flow field data during treatment, and drawing a particle sedimentation curve; performing sedimentation offset mapping fitting based on the preset ideal sedimentation conditions and the particle sedimentation curve, and generating particle sedimentation trajectory offset data;

[0009] Step S5: Obtaining sedimentation tank morphological parameters; reconstructing the sedimentation tank structure according to the sedimentation tank morphological parameters, and determining the reference inclination angle of the water inlet guide plate based on the sedimentation tank structure; predicting the abnormal state of sedimentation imbalance based on the reference inclination angle of the water inlet guide plate and the particle sedimentation offset data;

[0010] Step S6: analyzing abnormal exceeding limit parameters in the initial sewage treatment process model through retrospective analysis of the abnormal state of sedimentation imbalance; and correcting the model parameters of the initial sewage treatment process model based on the abnormal exceeding limit parameters to generate a corrected sewage treatment process model.

[0011] The present invention ensures comprehensive monitoring of the operating status of sewage treatment facilities through multi-point data collection of sewage treatment sensor networks. The extracted pipeline topology provides a clear spatial basis for subsequent data analysis. The position information marked after data projection enhances the traceability and practicality of the data. The construction of the initial sewage treatment process model ensures an accurate description of the sewage treatment process. The extracted sewage data set provides the necessary basis for model optimization. The simulated sewage treatment process environment generated by environmental simulation makes the simulation of the actual treatment situation more realistic. The simulation of virtual flow field data provides important support for particle motion analysis. Particle motion tracking and sedimentation curve drawing reveal the sewage treatment process. The particle behavior characteristics during the process and the generation of sedimentation offset mapping fitting provide a data basis for the optimization of the sedimentation process. The acquisition of sedimentation pool morphological parameters and the reconstruction of the sedimentation pool structure ensure a systematic understanding of the sedimentation process. The prediction based on the benchmark inclination angle of the guide plate and the sedimentation data can timely identify the abnormal state of sedimentation imbalance. The retrospective analysis of abnormal out-of-limit parameters effectively points to the adjustment direction of the model parameters. The sewage treatment process model after the model parameters are corrected improves the accuracy and applicability of the model, provides a reliable theoretical basis for the subsequent sewage treatment optimization, improves the overall operation efficiency and treatment effect of sewage treatment facilities, and promotes the intelligent and refined development of sewage treatment technology.

[0012] The present invention also provides a sewage treatment process model parameter correction system for executing the sewage treatment process model parameter correction method described above. The sewage treatment process model parameter correction system includes:

[0013] A data acquisition module is used to collect sensor data of multiple sewage treatment facilities through the sewage treatment sensor network; extract the sewage pipe topology of the sewage treatment sensor network; project the sensor data of multiple sewage facilities into the sewage pipe topology and mark the corresponding location information;

[0014] A model building module is used to build an initial sewage treatment process model based on corresponding location information and multi-point sewage facility sensor data; extract the treatment process sewage data set in the initial sewage treatment process model;

[0015] The environmental simulation module is used to simulate the sewage treatment environment based on the sewage treatment process data set and generate a simulated sewage treatment process environment; simulate virtual flow field data based on the simulated sewage treatment process environment and the sewage treatment process data set;

[0016] The particle tracking module is used to track particle motion based on the wastewater data set and virtual flow field data during treatment and draw particle sedimentation curves. It also performs sedimentation offset mapping fitting based on the preset ideal sedimentation conditions and particle sedimentation curves to generate particle sedimentation trajectory offset data.

[0017] The sedimentation analysis module is used to obtain sedimentation pond morphological parameters; reconstruct the sedimentation pond structure based on the sedimentation pond morphological parameters, and determine the reference inclination angle of the water inlet guide plate based on the sedimentation pond structure; and predict the abnormal state of sedimentation imbalance based on the reference inclination angle of the water inlet guide plate and particle settling offset data;

[0018] The parameter correction module is used to analyze the abnormal and excessive parameters in the initial sewage treatment process model through retrospective analysis of the abnormal state of sedimentation imbalance; based on the abnormal and excessive parameters, the model parameters of the initial sewage treatment process model are corrected to generate a corrected sewage treatment process model.

[0019] The present invention realizes comprehensive monitoring and real-time data acquisition of sewage treatment facilities through the application of data acquisition module, ensuring the accuracy and timeliness of data. The extracted sewage pipe topology structure provides a clear spatial framework for subsequent analysis. Data projection and location information marking enhance the traceability of data and effectively support the basis of model construction. The model construction module generates an initial sewage treatment process model based on multi-point sensor data, providing an accurate description of the treatment process. The extracted sewage data set provides the necessary basis for model optimization. The simulated sewage treatment process environment generated by the environmental simulation module ensures the effective simulation of the actual treatment situation. The simulation of virtual flow field data provides important support for particle motion analysis. The particle tracking module The drawing of sedimentation curves reveals the behavioral characteristics of particles during the treatment process. The generation of sedimentation offset mapping fitting provides a data basis for the optimization of the sedimentation process. The sedimentation pool morphological parameters obtained by the sedimentation analysis module provide a scientific basis for the reconstruction of the sedimentation pool structure. The prediction of the guide plate reference inclination angle can timely identify abnormal sedimentation imbalance states. The parameter correction module clarifies the direction of model parameter adjustment through retrospective analysis of abnormal out-of-limit parameters. The sewage treatment process model after model parameter correction significantly improves the accuracy and applicability of the model, provides reliable theoretical support for the optimized operation of sewage treatment facilities, improves the overall sewage treatment effect and efficiency, and promotes the intelligent and refined development of sewage treatment technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic flow chart of the steps of a method for calibrating parameters of a sewage treatment process model;

[0021] Figure 2 Detailed implementation flow chart of step S2;

[0022] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0023] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0025] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0026] To achieve this, please refer to Figures 1 to 2 , a method for correcting parameters of a sewage treatment process model, comprising the following steps:

[0027] Step S1: collecting sensor data of multiple sewage treatment facilities through a sewage treatment sensor network; extracting the sewage pipe topology of the sewage treatment sensor network; projecting the sensor data of multiple sewage facilities into the sewage pipe topology and marking the corresponding location information;

[0028] Step S2: constructing an initial sewage treatment process model based on corresponding location information and multi-point sewage facility sensor data; extracting a treatment process sewage data set in the initial sewage treatment process model;

[0029] Step S3: performing sewage treatment environment simulation based on the sewage treatment process data set to generate a simulated sewage treatment process environment; simulating virtual flow field data based on the simulated sewage treatment process environment and the sewage treatment process data set;

[0030] Step S4: Tracking particle motion based on the wastewater dataset and virtual flow field data during treatment, and drawing a particle sedimentation curve; performing sedimentation offset mapping fitting based on the preset ideal sedimentation conditions and the particle sedimentation curve, and generating particle sedimentation trajectory offset data;

[0031] Step S5: Obtaining sedimentation tank morphological parameters; reconstructing the sedimentation tank structure according to the sedimentation tank morphological parameters, and determining the reference inclination angle of the water inlet guide plate based on the sedimentation tank structure; predicting the abnormal state of sedimentation imbalance based on the reference inclination angle of the water inlet guide plate and the particle sedimentation offset data;

[0032] Step S6: analyzing abnormal exceeding limit parameters in the initial sewage treatment process model through retrospective analysis of the abnormal state of sedimentation imbalance; and correcting the model parameters of the initial sewage treatment process model based on the abnormal exceeding limit parameters to generate a corrected sewage treatment process model.

[0033] The present invention ensures comprehensive monitoring of the operating status of sewage treatment facilities through multi-point data collection of sewage treatment sensor networks. The extracted pipeline topology provides a clear spatial basis for subsequent data analysis. The position information marked after data projection enhances the traceability and practicality of the data. The construction of the initial sewage treatment process model ensures an accurate description of the sewage treatment process. The extracted sewage data set provides the necessary basis for model optimization. The simulated sewage treatment process environment generated by environmental simulation makes the simulation of the actual treatment situation more realistic. The simulation of virtual flow field data provides important support for particle motion analysis. Particle motion tracking and sedimentation curve drawing reveal the sewage treatment process. The particle behavior characteristics during the process and the generation of sedimentation offset mapping fitting provide a data basis for the optimization of the sedimentation process. The acquisition of sedimentation pool morphological parameters and the reconstruction of the sedimentation pool structure ensure a systematic understanding of the sedimentation process. The prediction based on the benchmark inclination angle of the guide plate and the sedimentation data can timely identify the abnormal state of sedimentation imbalance. The retrospective analysis of abnormal out-of-limit parameters effectively points to the adjustment direction of the model parameters. The sewage treatment process model after the model parameters are corrected improves the accuracy and applicability of the model, provides a reliable theoretical basis for the subsequent sewage treatment optimization, improves the overall operation efficiency and treatment effect of sewage treatment facilities, and promotes the intelligent and refined development of sewage treatment technology.

[0034] In an embodiment of the present invention, the method for calibrating parameters of a sewage treatment process model includes the following steps:

[0035] Step S1: collecting sensor data of multiple sewage treatment facilities through a sewage treatment sensor network; extracting the sewage pipe topology of the sewage treatment sensor network; projecting the sensor data of multiple sewage facilities into the sewage pipe topology and marking the corresponding location information;

[0036] In this embodiment, a sensor node network is deployed in the sewage treatment plant, and a multi-parameter water quality sensor of model YSI EXO2 is used to collect parameters such as dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH3-N) and water temperature (T) in the sewage network. The sampling frequency is set to 1 Hz, and the number of nodes is 50. The sensor nodes are distributed over the main sewage pipe, branch pipes and key nodes. A unique spatial number is set at each node, and the spatial coordinate annotation method based on the geographic information system (GIS) is used to extract the sewage pipe network topology information. The water quality data collected by each sensor is projected onto the corresponding sewage pipe network topology in the form of space-time triples (node ​​number, sampling time, measurement value). The topological relationship is used to determine the flow direction attribute and mark the pipe segment number, distance from the inlet, pipe diameter number and corresponding timestamp corresponding to each set of data, thereby completing the spatial mapping process of multi-point sensor data.

[0037] Step S2: constructing an initial sewage treatment process model based on corresponding location information and multi-point sewage facility sensor data; extracting a treatment process sewage data set in the initial sewage treatment process model;

[0038] In this embodiment, after obtaining spatially labeled sensor data, an initial model of the sewage treatment process is constructed. The sewage treatment unit process based on the double oxidation ditch process is selected as the modeling object. The initial model is constructed by combining data-driven modeling and physical rule modeling. The water quality data after the aforementioned mapping is used as input variables. The dynamic correlation between different measuring points is analyzed through the gray correlation analysis method, and an initial prediction model is established with influent water quality parameters as input and effluent water quality parameters as output. The initial model includes process variables such as reaction tank volume V (in m³), ​​sludge age SRT (in d), and reflow ratio R (in dimensionless ratio). After extracting intermediate data of the sewage treatment process stages in the initial model, a sewage data set for the treatment process is constructed. The data set includes dynamic change data such as DO, COD, NH3-N, water temperature, and flow rate every hour. The data set is segmented and coded according to time periods to form a daily sample set containing at least 720 records.

[0039] Step S3: performing sewage treatment environment simulation based on the sewage treatment process data set to generate a simulated sewage treatment process environment; simulating virtual flow field data based on the simulated sewage treatment process environment and the sewage treatment process data set;

[0040] In this embodiment, based on the above-constructed treatment process sewage data set, a three-dimensional treatment unit geometric model is constructed in the ANSYS Fluent simulation platform, the fluid properties are set to weakly compressible fluid, and the RNG ke turbulence model is used for simulation and solution. The fluid boundary condition is set to the water inlet flow rate Q=2000m³ / d in the actual collected data, the water inlet is the velocity inlet boundary condition, the water outlet is set to the pressure outlet, each time step is set to 1s, and the simulation period is 24h. A virtual sewage treatment environment such as the internal flow velocity distribution and vortex structure is generated through simulation. Subsequently, combined with the above-mentioned time-synchronized treatment process sewage data set, the data of each time step is mapped to the flow field cross-sectional position, and the Euler-Lagrange bidirectional coupling method is used to simulate the particle behavior in the virtual environment. The velocity, acceleration and displacement of each particle at each time node are extracted to form the virtual flow field particle motion data.

[0041] Step S4: Tracking particle motion based on the wastewater dataset and virtual flow field data during treatment, and drawing a particle sedimentation curve; performing sedimentation offset mapping fitting based on the preset ideal sedimentation conditions and the particle sedimentation curve, and generating particle sedimentation trajectory offset data;

[0042] In this embodiment, the virtual flow field data obtained above is input into the particle trajectory tracking module, and the particle initial radius r=0.05mm and the density =1.3g / cm³, using a particle tracking algorithm to perform hourly evolution calculations, tracking the complete trajectory of each particle from the water inlet to its sedimentation to the bottom of the pool, and plotting a two-dimensional functional relationship curve h(t) between the sedimentation height h and time t. The particle swarm optimization (PSO) algorithm is used to fit the difference between the sedimentation curve and the set ideal sedimentation model, and the fitting residual is defined as the sedimentation deviation ,The sedimentation trajectory offset dataset is generated by three-dimensional spatial interpolation, including the offset direction, offset velocity, and offset duration, and the spatial path deviation value of the particles in the actual structure is marked.

[0043] Step S5: Obtaining sedimentation tank morphological parameters; reconstructing the sedimentation tank structure according to the sedimentation tank morphological parameters, and determining the reference inclination angle of the water inlet guide plate based on the sedimentation tank structure; predicting the abnormal state of sedimentation imbalance based on the reference inclination angle of the water inlet guide plate and the particle sedimentation offset data;

[0044] In this example, a 3D laser scanning device (model Leica BLK360) was used to conduct field measurements of the existing sedimentation pond to obtain structural parameters such as the boundary contour, pool depth, pool length, and pool width of the sedimentation pond structure. The pool depth was 4.5 m, the pool length was 20 m, and the pool width was 10 m. These parameters were used to reconstruct the CAD geometric model of the sedimentation pond. Subsequently, the streamline projection method was used to determine the reference inclination angle of the water inlet guide plate based on the average flow velocity field value V0 = 0.35 m / s at the sedimentation pond inlet. , so that the initial streamline is tangent to the upper surface of the guide plate, the inclination angle The calculation formula is tan⁻¹(h / L), where h is the height difference between the end of the guide plate and the bottom of the pool, and L is the projected length of the guide plate. The angle is about 18°, which is used as the standard guide plate design benchmark. Combined with the aforementioned particle sedimentation offset data, the abnormal state of sedimentation imbalance is predicted based on the difference analysis between the sedimentation trajectory concentration area and the guide plate design vector.

[0045] Step S6: analyzing abnormal exceeding limit parameters in the initial sewage treatment process model through retrospective analysis of the abnormal state of sedimentation imbalance; and correcting the model parameters of the initial sewage treatment process model based on the abnormal exceeding limit parameters to generate a corrected sewage treatment process model.

[0046] In this embodiment, the time period corresponding to the above-mentioned predicted sedimentation imbalance area is traced back to the initial sewage treatment process model, the treatment parameters of the corresponding time period are matched, the fluctuation range of indicators such as DO, COD, and NH3-N within the time period is calculated, and the error between them and the standard input variables of the model is judged to be out of limit. When any parameter deviates from its optimal control range by more than 10%, it is marked as an abnormal out-of-limit parameter. For example, if DO drops from the normal value of 2 mg / L to 1.2 mg / L within a specific time period, it is judged to be out of limit. Subsequently, the corresponding DO model control parameter α in the initial model is corrected, and the least squares method is used to refit the mapping relationship between the inlet and outlet DO to complete the model parameter correction of the initial sewage treatment process model. Finally, the corrected model structure and parameter values ​​are output and synchronously recorded in the model version control library.

[0047] Preferably, step S1 includes the following steps:

[0048] Step S11: Designing the layout of key nodes of the sewage treatment facility to obtain sensor deployment site data; configuring a multi-modal acquisition unit for each sensor deployment site to construct a multi-point composite acquisition unit;

[0049] Step S12: constructing a sewage treatment sensor network based on the multi-point composite acquisition unit; performing multi-point synchronous sampling based on the sewage treatment sensor network to obtain multi-point sewage treatment facility sensor data, wherein the sampling frequency is set to 1 Hz to 5 Hz, and the sampling time is not less than 10 minutes;

[0050] Step S13: performing structured decoding processing on the sensor data of multiple sewage treatment facilities to obtain standardized sewage sensor node data;

[0051] Step S14: Analyze the sewage pipe topology data based on the sewage treatment sensor network, wherein the number of nodes is not less than 20 and the number of connected edges is not less than the number of nodes - 1;

[0052] Step S15: Projecting the multi-point sewage treatment facility sensor data onto the sewage pipe topology data to obtain location information mapping data, with the projection error threshold limited to ≤0.5m;

[0053] Step S16: performing site marking processing according to the location information mapping data to obtain corresponding location information of the multi-point sewage sensor projection data.

[0054] In this embodiment, according to the sewage treatment process flow chart and the positional relationship of each treatment unit, key nodes such as the sewage inlet, outlet, primary sedimentation tank, activated sludge tank, secondary sedimentation tank, and sludge return port are extracted, and the digital map platform ArcGIS is combined with the on-site BIM model for three-dimensional positioning analysis. The initial point layout simulation is performed based on the geometric distribution of the nodes and the main transmission path. The flow velocity, temperature, and pH distribution at the key nodes are simulated by the layout simulation software COMSOL Multiphysics, and representative sensor deployment sites are screened. Each site must meet at least one structural continuity constraint and two data stability thresholds to obtain sensor deployment site data. A multimodal acquisition unit consisting of a temperature sensor, a turbidity sensor, a pH sensor, and a laser particle monitoring unit is configured at each site. All units are assembled into a composite shell structure, the material of which is made of polytetrafluoroethylene and embedded with a corrosion-resistant platinum coating. A multi-point composite acquisition unit with a power module, a signal conditioning module, and an analog-to-digital conversion module is constructed. In the process of constructing the sewage treatment sensor network, In the process, the multi-point composite acquisition unit is connected to the Internet of Things data acquisition platform in the form of LoRaWAN (long-distance low-power wide area network), each node ID and its network protocol address are set, and a master-slave communication mechanism is established. The master node receives all slave node data and uploads it to the edge computing server. The structure of the sensor network is defined as G=(V,E), where V is the set of sensor deployment points and E is the set of network connectivity edges between nodes. The system sets the edge length threshold so that the shortest path between each two points does not exceed 25 meters, forming a sewage treatment sensor network structure with a stable topology. At the same time, multi-point synchronous sampling operations are started at each node, and the sampling frequency is set at 1 The sampling frequency is between Hz and 5Hz, and the sampling duration is not less than 600 seconds. Each data packet must contain a timestamp, a site number, four-channel original measurement values, a multimodal fusion identifier, and a time calibration tag. When performing structured decoding processing on the sensor data of multiple sewage treatment facilities, the HEX format data transmitted back by LoRaWAN is first restored to the original data through the edge server decoding module, and the fields are separated using the protocol parsing template. The parsed fields include channel type, sampled voltage value, temperature correction value, and timestamp. A field-level double check mechanism is used for CRC (cyclic redundancy check) error identification, and the correction rate is less than 95%. The data will be directly eliminated. After successful decoding, the unit conversion of each channel value is performed and the sensor source channel is marked. It is converted into standardized sewage sensor node data in a unified format and organized into a two-dimensional array according to the site number and sampling time. In the process of analyzing the sewage treatment sensor network, based on the aforementioned sensor deployment site data, an undirected graph structure is generated through the NetworkX library in Python. Each site is used as a graph node, and the physical distance between nodes less than 30 meters is set as a connected edge. The number of graph nodes in the network is required to be no less than 20, and the number of connected edges is no less than the number of nodes minus one, forming a basic topological structure that meets the minimum spanning tree constraint.The shortest path distance between all nodes is solved by Dijkstra algorithm and written into the topological adjacency matrix for subsequent sewage transmission path analysis. In the operation of projecting the multi-point sewage treatment facility sensor data into the sewage pipe topology structure data, the spatial position of the sensor site under the GIS coordinates and the direction segment and length information of each section of the pipeline in the topological structure are first obtained, and a three-dimensional projection matrix model is constructed. The shortest Euclidean distance criterion is used to map the spatial position points of the sensor nodes to the corresponding pipeline segment projection points. The maximum allowable projection error threshold is set to 0.5 meters, and the cosine angle calibration algorithm based on the coordinate point distance ratio is used to adjust the projection error. The data with errors exceeding the threshold range are eliminated. In the process of site marking processing based on the location information mapping data During the process, the data points retained after the projection operation are marked with position numbers in the sewage pipe structure according to their projection positions. The marking method adopts a three-layer nested structure. The first level is the trunk pipe number, the second level is the pipe segment number, and the third level is the sensor site number. The numbering rule is expressed in the form of "MPV", where M is the trunk number, P is the segment number, and V is the sensor number. At the same time, the number information and the corresponding standardized sensor data are written into the data index table to support the spatial node association call in the subsequent model parameter correction. The marking result finally generates the corresponding position information form of the multi-point sewage sensor projection data. The format is required to be CSV format. All fields are required to contain the site number, three-dimensional projection coordinates, pipe segment number and mapping error value field. The error is retained to three decimal places and the unit is meter.

[0055] Preferably, step S2 includes the following steps:

[0056] Step S21: performing position data matching on the corresponding position information and the multi-point sewage facility sensor data, setting the matching tolerance range to ±0.5 meters, and obtaining position information matching data;

[0057] Step S22: Process segment attribution is performed on the location information matching data. According to the node distribution in the sewage pipe topology, each matching point is assigned to the process segment closest to it. The attribution distance threshold is set to 1.0 meter to obtain process segment allocation data.

[0058] Step S23: screening the process section allocation data for processing characteristics, selecting data samples with water quality parameters in the range of pH 6.5–8.5, dissolved oxygen greater than 2 mg / L, and COD less than 300 mg / L, to obtain screening sensor data;

[0059] Step S24: performing time-series splicing on the filtered sensor data, splicing adjacent data in the order of acquisition timestamps, setting the maximum time interval to no more than 10 seconds, and obtaining a spliced ​​sensor data sequence;

[0060] Step S25: construct an initial sewage treatment process model based on the spliced ​​sensor data sequence; extract the process segment time series fragment data in the initial sewage treatment process model, wherein the minimum time span of the treatment unit is set to 30 seconds; extract the treatment process sewage data set based on the process segment time series fragment data.

[0061] In this embodiment, the position information matching process is performed on each record of the multi-point sewage sensor projection data generated in the previous stage. First, the three-dimensional spatial coordinates (x, y, z) and timestamp information in the record are extracted, and the Euclidean distance is calculated between them and the node spatial coordinates in the sewage pipe topology diagram. The matching tolerance range is set to ±0.5 meters, and the spatial matching formula is used. ,in( , , ) is the position of the sensor data point, ( , , ) is the topological node coordinate, if the calculation result If the distance between the sensor data record and the corresponding topological node is less than or equal to 0.5 meters, the sensor data record is successfully matched with the corresponding topological node and output as location information matching data. The matching operation uses the KDTree method in the scipy.spatial library in Python to construct an index tree structure for efficient matching. Each data output includes the sensor measurement value, measurement time, measurement spatial coordinates, and matching node number. The location information matching data is imported into the process segment attribution module. This module uses the sewage treatment units defined in the topological structure diagram as the basis for division. All nodes in the topological structure are divided into several process segment areas according to the treatment unit segment number. The Euclidean distance between the matching data point and the center node of each process segment is calculated. The process segment with the smallest distance that does not exceed 1.0 meter is selected as the assigned process segment. The attribution distance calculation uses the same three-dimensional spatial distance calculation formula as described above, with a distance threshold of d ≤ 1. 0 meters. If there are multiple process sections that meet the conditions, the process section corresponding to the pipe section number where the current point is located is matched first. The attribution operation is completed in the PostGIS spatial database. The ST_Distance function is used for spatial matching and screening. The attribution result is output as process section allocation data, which contains the following fields: original sensor data number, assigned process section number, matching distance, and assignment confirmation flag. The processing characteristics of each sensor parameter in the process section allocation data are screened. The pH value, dissolved oxygen DO concentration, and chemical oxygen demand COD value in each data are read. Samples with a pH range not between 6.5 and 8.5 are eliminated. Samples with dissolved oxygen DO lower than 2 mg / L are eliminated. Samples with COD higher than 300 mg / L are eliminated. The screening logic is implemented using NumPy array logical operations. The expression is set as: (pH ≥ 6.5) ∧ (pH ≤ 8.5).5) ∧(DO>2)∧(COD<300), all samples that meet the above conditions are retained. The data structure after screening retains the original field structure and adds a screening tag field to record whether each sample meets the screening conditions. Finally, the filtered sensor data set is output, and the filtered sensor data is sorted according to the acquisition timestamp. The data sample sets within each process section are grouped and processed. Each group of data is arranged in chronological order. The time intervals between adjacent samples are calculated. The maximum interval threshold is set to 10 seconds. Adjacent samples with a time interval less than or equal to 10 seconds are spliced ​​to form a continuous data segment. Samples with a time interval greater than 10 seconds are used as a new sequence. Starting point processing, the splicing operation uses the groupby method of the pandas library in Python to group according to the process section number, and then applies the rolling method window traversal to perform time difference calculation and splicing mark processing, and finally forms a spliced ​​sensor data sequence. Each sequence contains three parameter data points of pH, DO, and COD in a continuous time period. The data structure is adjusted to a list structure, including fields: sequence number, start time, end time, number of data points, and sequence data content. Each spliced ​​sensor data sequence is used as an input sample to construct an initial sewage treatment process model. The modeling dimensions are set to time series dimension and parameter dimension, and LSTM (Long A process sequence prediction model was constructed using a long-term short-term memory (LSTM) network model. A three-layer LSTM network structure was built using the Sequential model in the TensorFlow framework. The input dimension was (N, 3), where N was the number of time points in each data sequence and 3 was the parameter dimension (pH, DO, COD). The output was the predicted values ​​of the three parameters at the next time point. The training dataset set a minimum time span of 30 seconds for each segment. Sample sequences with less than 30 data points were not included in the modeling. The temporal trends of pH, DO, and COD in each segment were extracted as model training targets. After the initial model was output, the data in the model input sequence was uniformly classified as process segment time series data, maintaining the data structure consistent with the sequence splicing. A unified treatment process wastewater dataset was then constructed.

[0062] Preferably, step S3 includes the following steps:

[0063] Step S31: performing multi-zone pressure simulation on the sewage data set during treatment, and recording the pressure response to obtain the unit partial pressure characteristics;

[0064] Step S32: performing continuous permeation stratification processing on the unit partial pressure characteristics to obtain a layered permeation structure; performing flow channel configuration simulation on the wastewater data set during treatment based on the layered permeation structure to generate a simulated wastewater treatment process environment;

[0065] Step S33: performing distributed pressure control mapping based on the simulated sewage treatment process environment to obtain unit hydraulic distribution data; and reconstructing the hydraulic time series path based on the unit hydraulic distribution data;

[0066] Step S34: Perform path logic stitching simulation according to the hydraulic timing path, and construct a boundary ring structure based on the path logic stitching data; determine the flow boundary conditions based on the boundary ring structure and the hydraulic timing path, and simulate virtual flow field data based on the flow boundary conditions and the sewage data set of the treatment process.

[0067] In this embodiment, the sewage data set generated in the previous stage is imported into the three-dimensional finite volume multi-zone pressure simulation system to construct a simulation model based on the spatial process segment division. The model uses the interFoam solver module in the OpenFOAM framework, and the size of the simulation domain is set according to the actual size of each process segment. The three-dimensional grid is constructed with 0.1 meters as the unit, and the boundary conditions are set as a stable flow rate inlet (1.5 meters / second) and a constant pressure outlet (101325 Pa). Each treatment unit area is used as an independent simulation area, and the partition definition is completed through the grid partition control dictionary file blockMeshDict. The hydraulic parameters in the sewage data set are input into the model as the initial field variables, and the transient solution is performed using the PISO pressure iteration algorithm, which is recorded in The pressure change data of each unit area in each time step (the time step is set to 0.1 seconds) are used to derive the pressure response curve of each unit within the simulation time. The average pressure gradient, response peak value, and response delay time of each unit are output as characteristic indicators to generate unit partial pressure characteristics including unit number, spatial position, average response value, maximum pressure, and response time. The unit partial pressure characteristics are input into the three-dimensional permeation stratification analysis system. The flow direction vector field is constructed according to the direction of the average pressure gradient between each unit. The three-dimensional layered interpolation algorithm is used to divide the entire pressure field into multiple continuous permeation layers. The layering threshold is set to 0.2 Pa / meter. The continuous area with pressure gradient change is divided into one layer based on this threshold. The layering operation uses the piecewise linear interpolation module in MATLAB. At least three layers of permeation layer structure data are output in each process section. The structure data includes layer number, start and end Z coordinates, average pressure value and pressure change rate. The layered structure is imported into the fluid element simulation platform. Combined with the flow velocity and flow information in the sewage data set, the Ansys Fluent simulation module is used to construct the flow channel configuration model. The turbulence model is set to The model is constructed with the inlet conditions uniformly set to a constant velocity (1.5 m / s) and a temperature constant (298 Kelvin). The channel configuration boundary is defined according to the pressure distribution and flow direction data inside different permeable layers, and a complete simulated sewage treatment process environment is output. The simulation output data includes a velocity field distribution diagram, a channel structure element model, and a node connection table. The boundary structure and internal flow velocity information of each treatment unit in the simulated sewage treatment process environment are extracted, and a distributed pressure control mapping model is constructed. The volume pressure mapping of each unit is generated using the volScalarField structure in OpenFOAM. The injection value is used to map each volume unit to the corresponding processing unit number, and the unit hydraulic distribution data structure is constructed. The fields include the unit number, unit volume pressure, pressure difference vector and flow direction unit vector. The hydraulic path reconstruction operation uses the Dijkstra shortest path algorithm to construct a weight matrix in a directed graph with each processing unit as a node. The weight value is the ratio of the unit pressure difference between the two units to the path length. After constructing the path graph, it iteratively outputs the maximum hydraulic flux path starting from the inlet unit, outputs the connection nodes, pressure change value and time label of each time step in the path sequence, and completes the hydraulic time series path reconstruction. Read the connection sequence and unit boundary topology in the hydraulic time sequence path, perform path logic stitching simulation operation, and connect the boundary surfaces of adjacent path nodes. The judgment standard is that the angle between the coplanar boundary normal vectors of the two nodes is less than 10 degrees and the relative position distance is less than 0.1 meters. The stitching logic uses a triangular mesh splicing algorithm to insert connection surface nodes in the coplanar area to form a boundary closed loop. All closed loop areas are extracted as a boundary loop structure set. The closed loop path number, the number of boundary surfaces, the stitching point coordinates, and the surface normal vector set are recorded in the structure. After constructing the geometric model of each boundary loop, the hydraulic path is combined with the The flow and pressure data generate the flow boundary conditions for each boundary. The boundary conditions are mainly constant pressure boundaries and constant flow velocity boundaries. The specific condition values ​​are extracted from the sewage data set of the treatment process, and the average flow velocity and average pressure difference are used as set values. All boundary conditions and sewage data sets are imported into the virtual flow field simulation module to perform the three-dimensional transient flow field simulation process. The simulation module uses the PISO pressure iteration method based on the control volume method. The time step is set to 0.05 seconds and the total simulation time is set to 60 seconds. The exported virtual flow field data contains the velocity vector, pressure scalar and flow direction unit vector of each grid point at each time step.

[0068] It is particularly important that step S34 includes:

[0069] Perform segment association on the hydraulic time series path to obtain candidate nodes for path stitching;

[0070] Perform stitching logic fitting based on the path stitching candidate nodes to generate path logic stitching data;

[0071] Construct adjacent boundary rings by stitching data according to path logic, thus obtaining a boundary ring structure;

[0072] Perform boundary state mapping on the boundary ring structure data and hydraulic time series path to generate flow boundary conditions;

[0073] The virtual fluid field configuration is processed based on the flow boundary conditions and the sewage data set of the treatment process to obtain the virtual flow field data.

[0074] In this embodiment, the hydraulic time series path is imported into the path segment analysis module. The node sequence in each path is segmented, with each segment length set to 1.5 meters. The continuous node sequence in the path is divided into multiple segments based on distance conditions. The spatial proximity of the end nodes of adjacent segments is determined, with a threshold of 0.15 meters. If the Euclidean distance between the end nodes of two segments is less than the threshold, the node is marked as a path stitching candidate node. The degree of the node in the path topology graph is calculated, and nodes with a degree less than 2 are filtered out to avoid interference from isolated endpoints. The node numbers and coordinate information of all nodes that meet the requirements are output as a path stitching candidate node table. The data structure includes candidate node numbers, spatial coordinates, adjacent path numbers, and segment vector directions. The path stitching candidate node table is input into the logic fitting module, and stitching logic is constructed using a fitting algorithm based on the angle between three-dimensional vector directions. For each pair of path segment direction vectors, the angle between them is calculated and determined to be less than 30 degrees. If the angle meets the angle requirements and the distance between the nodes does not exceed 0.If the distance is 15 meters, it is determined that there is a potential logical stitching relationship between the two nodes, and a stitching logical connection edge is generated. The graph structure data is used to record it. After constructing the logical stitching graph, the Kruskal minimum spanning tree algorithm is used to traverse the edge set in the graph, and the minimum path set that forms a ring structure or a cross path is extracted. The node start and end coordinates, stitching angle and stitching distance of each stitching edge are marked, and a path logical stitching data set is generated at the same time. The data structure contains the logical edge number, starting node, target node, stitching vector and spatial angle. The path logical stitching data is imported into the boundary ring generation module, and each logical stitching edge is traversed in turn. According to its connection relationship, The closed-loop path is extracted, and the boundary connection surface splicing processing is performed on each path structure that forms a closed loop. A planar connection surface is constructed in three-dimensional space based on the two endpoints of the stitching edge. The Delaunay triangulation method is used to generate the surface mesh. The minimum number of faces for each boundary ring is set to 8, and the maximum normal vector difference threshold is 20 degrees. Those exceeding this value do not participate in the closed-loop structure construction. The topological nodes, boundary meshes, triangulation surfaces, and boundary vector direction information of each closed-loop structure are uniformly encapsulated into boundary ring structure data, which includes the boundary ring number, the number of stitching logic edges included, the node set, the boundary mesh file path, and the direction tensor matrix. , the pressure gradient change values ​​of the upstream and downstream of the path segment are extracted from the boundary ring structure data and the hydraulic time series path data, and the flow direction attribute is assigned to each boundary ring based on the principle that the flow direction is from the high-pressure area to the low-pressure area. The boundary inlet type is determined by the angle between the velocity vector of each node and the normal vector of the boundary ring. If the angle is less than 45 degrees, it is defined as the inlet boundary, otherwise it is defined as the outlet boundary. The inlet boundary is set with a constant pressure boundary condition, and the pressure value is set to the maximum pressure of the node in the path. The outlet boundary is set as a constant flow velocity boundary, and the flow velocity value is the average flow velocity of the downstream path node. All boundary conditions are uniformly stored in the flow boundary condition dictionary, including the boundary number, The boundary type, pressure value or flow velocity value, boundary normal vector and corresponding path number were input into the virtual fluid field configuration platform. The platform used Fluent, a CFD-based multi-physics field joint modeling environment, to construct a complete sewage treatment channel structure element model. The computational domain elements were generated using the Tet meshing method. The minimum element size was set to 0.05 meters and the maximum element size did not exceed 0.3 meters. The simulation time step was set to 0.05 seconds, and the total simulation time was 120 seconds. The fluid properties were set to incompressible Newtonian fluid, with a density of 998 kg / m³ and a dynamic viscosity of 0.001Pa·s, using the k-ωSST turbulence model to handle near-wall flow. The model introduces dissolved oxygen, pH, and COD values ​​from the sewage dataset as physical variables to participate in the flow field solution. After the boundary conditions are loaded, a three-dimensional transient simulation is performed, and the velocity vector, pressure distribution, and pollutant diffusion fields in the flow field at each time step are derived and uniformly output as structured data in HDF5 format, including the velocity, pressure, and sewage composition index values ​​of each voxel at each moment.

[0075] Preferably, the step S4 of tracking particle motion based on the wastewater data set and the virtual flow field data during treatment and drawing a particle settling curve includes:

[0076] Analyze the fluid shear field based on virtual flow field data, and infer shear stress distribution data based on the fluid shear field;

[0077] Identify wastewater particle parameters in a process wastewater dataset;

[0078] Combine shear stress distribution data and wastewater particle parameters and establish particle-fluid interaction relationships;

[0079] Identify particle force data based on particle-fluid interaction relationship and shear stress distribution data;

[0080] Perform time domain propulsion simulation based on particle force data and record the particle motion trajectory in the simulated propulsion time domain data;

[0081] Extract sedimentation rate characteristics from particle motion trajectories and analyze the time-varying sedimentation rate of particles;

[0082] Draw a particle sedimentation velocity curve based on the time-varying sedimentation velocity of the particles;

[0083] The particle settling rate curve is converted into a displacement representation, thereby obtaining a particle settling displacement curve;

[0084] Identify the time relationship in the particle settling displacement curve, and draw the particle settling curve based on the time relationship and the particle settling displacement curve.

[0085] In this embodiment, the three-dimensional virtual flow field data is used to divide the entire processing area into equidistant cubic grids at a spacing of 20 cm. The central difference method is used to perform directional difference operations on the velocity field data in each grid to obtain the velocity change rate of the fluid in each dimensional direction. The numerical flow boundary modeling module in the finite volume method is called, and the dynamic viscosity parameter set for the fluid (set to 0.001 Pas in this embodiment) is combined to perform shear field partitioning on the velocity change area. A shear stress point distribution mapping matrix is ​​established for all areas with large velocity gradients. Each matrix point is bound to the current position coordinates, the corresponding velocity change value, and the inferred local fluid stress value. Finally, a matrix containing the three-dimensional coordinate index and shear stress is output. The multidimensional data array of data values ​​reads the sewage particle image data and particle physical parameter record table collected at the experimental site. The particle image data is obtained by the microscopic image acquisition system. Each frame of the image covers an area of ​​2 square millimeters. The system collects 5 frames of data per second. After extracting the particle outline boundary using the edge detection method, the projected area and contour perimeter of each particle are obtained based on the pixel distribution in the binary image. The particle shape factor is obtained by image ratio. At the same time, a high-precision laser particle size analyzer is used to statistically measure the particle size in the corresponding sample. The measured particle size range is between 1 micron and 15 microns. The average density is determined by particle sedimentation experiments. The value range after the float method is 1.05 grams per cubic centimeter to 1.4 grams, all data are uniformly numbered and stored as a particle basic attribute table, including particle size, density, shape factor and image number. The particle size, density and shape factor of each particle are used as input parameters. According to its position in the shear stress field, the local stress value of the corresponding grid position is extracted from the shear stress distribution data. Then, according to the interaction law between the stress value and the particle properties in space, a data model of three types of force terms is constructed, which correspond to the fluid resistance, buoyancy and sinking tendency of the particle respectively. The data structure nesting method is used to construct an independent force vector data structure for each particle. At the same time, the particle position and its local stress environment are updated at each simulation time step, and the evolution path of the interaction is recorded. The interaction results between all particles and the fluid are stored in a three-dimensional array, which contains the phase relationship of each particle at each time point. The force state, position index, and current shear field environment number are used as the index. The particle number is used to traverse its position data at each simulation time step. The shear stress value of the grid in which it resides and the velocity difference of the adjacent grids are extracted using a spatial position correspondence method. These two data items, combined with the particle size and density in the particle attribute data table, are used to retrieve the current fluid interaction intensity term for the particle in the data interaction model. The gravity influence term and the buoyancy vector term are also added simultaneously to construct a complete three-dimensional force data vector, recording the direction and magnitude of all force vectors acting on the particle in that time step. The final output is a continuous-time force table for each particle, including the total force magnitude, direction unit vector, shear field number, and historical path information for each time step. This table serves as the driving source for the next trajectory advancement. Each simulation step is set to 0.02 seconds, read the force direction and magnitude of each particle at the initial moment from the force data table, and calculate the new position of the particle step by step according to the force state and velocity direction at the previous time point. Record the new position every time it is advanced and store it in the trajectory table together with the force value. The system sets the maximum advancement time of the particle in the entire simulation process to 30 seconds, and generates no more than 1500 trajectory points for each particle. The trajectory is output in CSV format, and each line is a time step containing the coordinate point, velocity direction and force intensity identifier of the time. The trajectories of all particles form a set of three-dimensional space motion paths. The displacement change of the particle in the vertical direction is extracted from the above trajectory set, and the displacement change of the particle in the vertical direction is calculated according to each time step. Interval sampling is performed to calculate the vertical distance difference between the coordinate points of two adjacent frames to obtain the sinking velocity of each time step. Then, the velocity sequence is smoothed by sliding windows, and the values ​​are averaged every five frames as a window to reduce the impact of single-point abnormal fluctuations. The sedimentation velocity sequence of each particle in the entire time period is sorted out. The maximum value, minimum value and change slope of the sequence are used to analyze the trend of particle sedimentation velocity over time. Finally, a sedimentation velocity time series data table of each particle is output, and the start and end times of the acceleration segment, stable segment and fluctuation segment are marked. The sedimentation velocity data table is imported into the graphics drawing program, and the horizontal axis is set as the time step and the vertical axis is the sedimentation velocity at the corresponding moment. A line graph is used to draw the sedimentation velocity curve of each particle, and the curves are grouped and displayed. Particles in different particle size ranges are distinguished by different colors, and the legends are marked as "particle size less than 5 microns", "particle size between 5 and 10 microns", and "particle size greater than 10 microns". The image size is set to 12 cm per side and the resolution is 300 dots per inch. The sedimentation rate curve data of each particle is read, and the velocity data and the time step length of each time step are accumulated and calculated to obtain the vertical displacement of the particle from the initial moment to the current time point. The displacement values ​​are arranged in chronological order to generate a displacement sequence and stored as a particle displacement time table. The particle sedimentation displacement curve is drawn, and the horizontal axis is time. The vertical axis represents displacement distance, and the curve trend shows the trajectory of the vertical distance traveled by the particles throughout the settling process, with units uniformly expressed in millimeters. Three key time points are identified from the particle settling displacement curve: the starting point of continuous settling, the beginning of the velocity stabilization phase, and the end of the settling process. These time points and the corresponding displacements are annotated on the graph, and the curve is segmented to create a complete settling trend chart. The settling curve is presented as a smooth curve, with the start and end times and settling distances of each segment noted. Settling curves for particles of different sizes are compared, and the output is a superimposed chart containing multiple curves for use in settling behavior clustering, feature extraction, or model fitting.

[0086] Preferably, the sedimentation offset mapping fitting based on the preset ideal sedimentation condition and the particle sedimentation curve in step S4 includes:

[0087] Draw an ideal deposition curve based on the preset ideal deposition conditions;

[0088] Project and compare the ideal sedimentation curve and the particle sedimentation curve, and construct the sedimentation difference matrix;

[0089] The offset of each particle trajectory is calculated according to the sedimentation difference matrix and integrated into a particle trajectory offset set;

[0090] Determine the standard particle motion trajectory based on the ideal deposition curve;

[0091] The particle trajectory offset set is superimposed on the standard particle trajectory, and an offset correction is performed to obtain an offset-corrected trajectory set.

[0092] Perform spatiotemporal clustering on the offset-corrected trajectory set to obtain trajectory correction point data;

[0093] Identify key excursion patterns based on trajectory correction point data;

[0094] The particle sedimentation curve is predicted to be offset through the key offset mode to generate particle sedimentation trajectory offset data.

[0095] In this embodiment, according to the preset ideal deposition situation, the basic parameters required for the ideal deposition curve are first defined. These parameters include the particle size, density and dynamic characteristics of the fluid. In this embodiment, the particle size range is set to 5 microns to 10 microns, the density is 1.2 g / cm3, and the dynamic viscosity of the fluid is set to 0.0012 Pas. In addition, a deposition rate model is required. The model assumes that the deposition rate of particles in a static fluid environment follows Stokes' law. In the process of calculating the particle velocity, it is assumed that the fluid temperature is 25°C and the density of water is 1 g / cm3. Using these set physical parameters, calculations are performed according to the deposition rate formula to draw an ideal particle deposition curve. The curve shows the sedimentation of particles under ideal conditions. The relationship between speed and time is calculated, and the curve is used as a reference benchmark in the subsequent correction process. Through numerical simulation, the sedimentation curve of each particle in the actual sewage treatment process is obtained. These sedimentation curves are constructed based on the aforementioned particle force data and fluid shear field environment. The time span of the sedimentation curve and the change in particle sedimentation position are recorded within 30 seconds. Each time step generates data on the relationship between the vertical sedimentation displacement of the particle and the time point. In the particle sedimentation curve, the real-time position of each particle is compared with the ideal sedimentation curve, and its deviation is measured to construct a sedimentation difference matrix. The rows and columns of the matrix represent the particle number and time step respectively. The elements in the matrix are the difference values ​​between the actual sedimentation displacement of the particle in the time step and the corresponding displacement of the ideal sedimentation curve. The matrix will The offset at each moment is represented in detail. Each row of the difference matrix shows the offset state of the particle at different time points. The larger the value in the matrix, the more obvious the difference between the actual sedimentation and the ideal deposition, and vice versa. The result of the sedimentation difference matrix is ​​matched with the trajectory measured for each particle during the actual processing process. The difference between the actual sedimentation position of each particle at each time point and the ideal position of the ideal deposition curve is compared one by one, and the offset of each particle is calculated. The offset is obtained by subtracting the corresponding position on the ideal deposition curve from the actual sedimentation position. The offset of all particles at each time step will form a particle trajectory offset set. Each offset data point consists of a particle number, a time step, and an offset. The offset set will record the position of all particles in the simulation process. The trajectory correction data in the scatter plot is used to integrate the offset data of all particles. According to the ideal deposition curve, the motion trajectory of each particle in the ideal situation is calculated and determined at each time step, and the standard trajectory is set during the deposition process. The standard trajectory assumes that all particles sink steadily in the same fluid environment according to Stokes' law and the dynamic characteristics of the particles. Based on this standard trajectory, the ideal motion trajectory of each particle is defined. This trajectory serves as the sinking path of the standard particles and is not affected by external disturbances. It only considers the natural sedimentation velocity in the fluid environment. Each time point of the ideal deposition curve corresponds to a specific set of particle sinking positions. In this process, the offset data of the particles is determined only by the fluid dynamics and the physical properties of the particles, and has nothing to do with the slight disturbances of the external flow field.The obtained canonical particle motion trajectory is the sinking path of each particle under ideal conditions, which is the reference standard for subsequent offset correction and spatiotemporal distribution cluster analysis. By matching the particle trajectory offset set with the canonical particle motion trajectory one by one, the offset is added to the position in the canonical trajectory at each time step to obtain the corrected trajectory of each particle. The calculation formula of the offset corrected trajectory is: corrected position = canonical position + offset. The corrected trajectory set will include the corrected position of each particle at each time step. The corrected position represents the offset correction effect of the particle in the actual sewage treatment process due to external factors. The offset corrected trajectory set of all particles will serve as the basis for subsequent analysis to further calibrate the particle motion trajectory to ensure that the particle behavior in the sewage treatment process conforms to the actual fluid environment. The corrected particle trajectory data will be subjected to spatiotemporal distribution analysis. The cluster analysis method is used to spatially cluster the corrected positions of all particles at different time steps, analyze the distribution law of the particle corrected position, and use K-mean The s (K-means) clustering algorithm divides the particle trajectory correction points into several groups according to their similarity. Each group of particles shows similar deviation trends under similar spatiotemporal conditions. The clustering algorithm automatically groups the spatiotemporal distribution data of particles by setting a predetermined number of clusters. The particles in each cluster show relatively consistent sedimentation behavior. The clustering result forms a trajectory correction point data set, which records the trajectory correction points of particles belonging to the same spatiotemporal cluster in the corrected motion trajectory of the particles. The clustered data set will provide trend information of the particle trajectory. According to the results of clustering analysis, for each trajectory correction point data, Pattern recognition uses data mining technology to extract the spatiotemporal characteristics of each particle trajectory and identify common offset patterns of particle motion trajectories under different sedimentation conditions. During the pattern recognition process, the data of each corrected trajectory is first smoothed to remove noise and outliers. Then, the regularity of the corrected trajectory is analyzed to identify time periods or regions with significant offsets, and these key offset patterns are recorded. Offset patterns can be classified based on factors such as particle settling rate changes and settling stability. Each pattern corresponds to a specific type of settling behavior. Identifying these key offset patterns helps to accurately predict and control the subsequent particle settling process. The identified key offset patterns are used to predict the offset of the particle settling curve. At each time period of particle settling, the particle settling curve is corrected based on the identified offset patterns, and the particle settling position offset in the future time step is estimated to generate new particle settling trajectory offset data. The prediction results are calculated based on the initial state of the particle, the fluid environment, and the key offset patterns. The generated particle settling trajectory offset data provides a correction value for the particle position at each time step. Based on this correction data, the settling behavior of particles in the sewage treatment process can be adjusted and optimized in real time. ,

[0096] Preferably, in step S5, reconstructing the sedimentation tank structure according to the sedimentation tank morphological parameters and determining the reference inclination angle of the water inlet guide plate based on the sedimentation tank structure includes:

[0097] Construct the initial sedimentation tank framework based on the sedimentation tank morphological parameters;

[0098] Identify the key structural nodes in the initial sedimentation tank framework and connect the nodes based on the key structural nodes to generate the sedimentation tank skeleton network;

[0099] Identify the geometric skeleton of the inlet area in the sedimentation tank skeleton network;

[0100] The guide plate position and angle are calculated based on the geometric skeleton of the water inlet area, thereby generating a guide plate parameter set;

[0101] The reference inclination angle of the water inlet guide plate is determined based on the guide plate parameter set of the water inlet area geometric framework.

[0102] In this embodiment, the size of the sedimentation tank is set according to the specific morphological parameters of the sedimentation tank, including basic information such as the length, width, height of the tank body and the slope of the tank bottom. In this embodiment, it is assumed that the sedimentation tank is 20 meters long and 10 meters wide, the slope of the tank bottom is 5 degrees, and the water depth in the tank is 3 meters. Using these parameters, the initial three-dimensional framework of the sedimentation tank is first constructed, and the geometric shape of the tank body is modeled using CAD (computer-aided design) software. By setting the inclination of the tank bottom and the boundary of the tank wall, a geometric model of the sedimentation tank is established. This model provides the basis for the overall shape of the sedimentation tank. At the same time, it is necessary to ensure that each boundary condition is consistent with the given The physical parameters of the sedimentation tank are matched, and the stability of the pool body and the fluid flow characteristics are taken into consideration during the construction process. In the initial framework of the sedimentation tank, the nodes with key structural significance are identified. These nodes mainly include the pool wall connection points, the intersection points between the pool bottom and the pool wall, and the interface points of the guide plate installation position. In this embodiment, the detailed three-dimensional point cloud data of the sedimentation tank frame is obtained by 3D scanning technology, the point cloud data is processed, the key structural nodes are screened out, and the relationship between the nodes is analyzed by geometric modeling tools to determine the connection method and relative position of each node. Then, based on these key nodes, the nodes are connected to construct the sedimentation tank. The skeleton network of the sedimentation tank is composed of various nodes and connecting lines, which represent the structural framework of the sedimentation tank. By analyzing the geometric features in the skeleton network of the sedimentation tank, especially the areas related to the inlet flow rate and flow direction, the geometric skeleton of the inlet area in the skeleton network of the sedimentation tank is identified. In this step, the position of the water inlet is first set. It is assumed that the water inlet is at the entrance of the sedimentation tank. Then, according to the geometric structure of the sedimentation tank and the principle of fluid dynamics, the fluid flow direction of the water inlet area is calculated. The fluid flow is simulated using CFD (computational fluid dynamics) simulation software to obtain the fluid streamlines, and then the factors affecting the inlet flow rate and flow direction are identified. Key geometric features: The geometric skeleton of the water inlet area is determined by outlining the water inlet streamlines and area boundaries in three-dimensional space to determine the actual geometric shape of the water inlet area. The geometric skeleton reflects the main path of fluid flow and the shape of the area. Based on the geometric skeleton of the water inlet area and the requirements of fluid flow, the appropriate deflector position and angle are calculated. In this step, the geometric skeleton information of the water inlet area is used to first determine the installation position of the deflector. The selection of the installation position is based on the flow direction and flow velocity requirements of the water inlet. Then, the optimal angle between the deflector and the water inlet flow is determined through fluid dynamics calculations. Fluid simulation software (such as ANSYS Fluent) is used to simulate the deflector design. By simulating the fluid flow at different angles of the deflector, the optimal angle is obtained to make the fluid flow more uniform, thereby achieving an ideal fluid distribution effect. Finally, based on the simulation results, the specific parameters of the deflector, including its position, angle, size, etc., are set. These deflector parameter data constitute the deflector parameter set. By analyzing the geometric framework of the water inlet area and the deflector parameter set, based on the fluid dynamics calculation and simulation results, the base inclination angle of the water inlet deflector is determined.First, a comprehensive analysis of the inlet area's geometric framework and the guide plate's parameter set was conducted to calculate the flow velocity and direction changes before and after the guide plate. Using CFD simulation results, the guide plate's angle was optimized to evenly distribute the fluid along a predetermined trajectory into the sedimentation tank. Ultimately, a baseline tilt angle was determined. In this example, through testing and simulation, a 30-degree guide plate baseline tilt angle was determined. This angle effectively guides fluid into the sedimentation tank and minimizes unnecessary turbulence in practical applications. Multiple simulations and adjustments were then conducted to confirm the rationality and effectiveness of this baseline tilt angle, ultimately resulting in the final design value for the guide plate's baseline tilt angle.

[0103] Preferably, the step S5 of predicting the abnormal state of sedimentation imbalance based on the reference inclination angle of the water inlet guide plate and the particle sedimentation offset data includes:

[0104] The long-term sedimentation process is simulated by using the particle settling trajectory migration data and sediment pool morphology parameters, and the cumulative distribution of sediments is determined based on the long-term sedimentation process;

[0105] Calculate sediment load distribution based on the cumulative distribution of sediments and generate structural load distribution data;

[0106] Map the structural load distribution data to the initial sedimentation tank framework and perform finite element stress analysis to generate stress distribution data;

[0107] Identify the stress concentration points of the guide plate based on stress distribution data and sedimentation pool morphological parameters, and analyze the stress state of the guide plate;

[0108] Predict the deformation trend of the guide plate according to its stress state;

[0109] The rate of change of the inclination angle over time is calculated based on the deformation trend of the guide plate and mapped into a dynamic evolution curve of the guide plate inclination angle;

[0110] The future tilt angle change data is predicted through the dynamic evolution curve of the guide plate tilt angle and the reference tilt angle of the water inlet guide plate;

[0111] Performing surface fitting processing on the future tilt angle change data to obtain a tilt angle change fitting surface;

[0112] The fitting surface of the tilt angle change is segmented for temporal stability to generate change trend fracture segments;

[0113] Based on the fracture fragment mapping of the change trend, the corresponding deflector structure response area;

[0114] The flow field deformation state is deduced based on the corresponding guide plate structure response area and particle sedimentation offset data;

[0115] Perform gradient analysis on the deformation state of the flow field, calculate the corresponding velocity change rate of the guide plate area, and generate the flow field disturbance intensity;

[0116] An intensity comparison is performed based on the preset critical sedimentation conditions and the flow field disturbance intensity, and the abnormal state of sedimentation imbalance is predicted based on the intensity comparison results.

[0117] In this example, a long-term simulation of the sedimentation process was performed using sedimentation pond morphological parameters and particle settling trajectory offset data. Assuming a total depth of 3 meters and a width of 10 meters, particle physical parameters such as particle size and density (e.g., a particle diameter of 0.5 mm and a density of 2.5 g / cm³) and information such as the water velocity in the sedimentation pond were used in conjunction with the sedimentation dynamics equation. Particle tracking simulation technology was used to simulate the particle settling process at different time points. The particle settling trajectories within the pond were obtained using a fluid dynamics model. The distribution of each particle at the pond bottom was gradually accumulated to obtain cumulative sediment distribution data. This data demonstrated the thickness and distribution density of sediment in different regions of the pond. The simulation results helped accurately reflect the sedimentation dynamics of the sedimentation pond and ensured that the simulation matched the actual sedimentation environment. The simulation spanned 6 months, with particle positions updated daily. Sediment distribution was determined through multiple iterations of the simulation. Based on the cumulative sediment distribution, the sediment load distribution was calculated. Assuming a sediment density of 1.5 g / cm³, the sediment thicknesses at different regions of the pond bottom were 0.5 meters, 1.2 meters, and 0.1 meters, respectively.8 meters, the sedimentation load calculation formula is load = density × gravitational acceleration × sediment volume. By applying this formula in different areas of the sedimentation tank, the sedimentation load of each area is obtained. Further, based on the load distribution data, the structural load distribution data is generated. Considering the impact of the sedimentation load on the sedimentation tank structure, combined with the structural characteristics of the tank body, the load distribution data is matched with the stress state of the tank wall, tank bottom and other positions to generate a sedimentation load distribution diagram. This diagram can be used to further analyze the stress condition of the structure and provide necessary data for subsequent finite element analysis. The distribution of load in the calculation process depends not only on the thickness of the sediment, but also on the flow rate of the water and the shape of the tank body. The load changes caused by the irregular shape of the tank bottom are taken into account in the actual calculation. The structural load distribution data generated in the previous step is mapped to the initial sedimentation tank framework and the finite element analysis (Finite Element The stress analysis of the sedimentation tank was carried out using the FEA (Finite Element Analysis) method. First, the load data was input into the finite element analysis model to establish a finite element model of the sedimentation tank. It was assumed that the thickness of the sedimentation tank wall was 30 cm and the thickness of the pool bottom was 50 cm. By refining the grid, the sedimentation load distribution was applied to each unit, and stress calculation was performed to obtain stress distribution data. Stress simulation was performed using software such as ANSYS. By carefully dividing the structure of the sedimentation tank into grids, the stress conditions of the sedimentation tank under different loads were simulated. The results showed that the stress changes in the sedimentation tank structure in different areas, especially in the stress concentration areas of the pool wall and pool bottom, the simulated stress data provided an accurate basis for subsequent analysis. Based on the stress distribution data and the morphological parameters of the sedimentation tank, the stress concentration of the guide plate was identified. First, the geometry and position of the guide plate are input into the stress analysis model. Assuming that the guide plate is located in the water inlet area and has an inclination angle of 30 degrees, the stress distribution on the guide plate under different loading conditions is calculated by meshing the guide plate surface, and the areas with the highest stress are identified. These areas are the stress concentration points. In this process, the maximum stress area of ​​the guide plate is determined using stress field data. Assuming the maximum stress value is 200MPa, the stress field is used to analyze the force condition of the guide plate, and then analyze whether the guide plate is at risk of damage or deformation. Based on the changes in the stress concentration points of the guide plate, the design is further optimized to reduce the risks caused by stress concentration. Based on the aforementioned stress state of the guide plate, the deformation trend of the guide plate is predicted. First, the stress-strain relationship (e.g. ,in is stress, is the elastic modulus, is strain) to calculate the deformation degree of the guide plate under different stresses, and use the maximum stress value obtained in the previous calculation and the elastic modulus of the guide plate material to obtain the deformation degree. Assuming that the material of the guide plate is steel and its elastic modulus is 210GPa, the calculation results show that under the maximum stress, the deformation of the guide plate is 0.001mm. Based on this data, it can be inferred that the deformation trend of the guide plate will occur during long-term use. The gradual accumulation stress analysis method is used to predict the long-term deformation of the guide plate, and finally the trend of its deformation degree over time is calculated. According to the deformation trend of the guide plate, the change rate of its inclination angle is calculated. Assuming that the initial inclination angle of the guide plate is 30 degrees, based on the deformation trend, the inclination angle change rate is set to 0.01 degrees / hour. After a certain period of time (for example, 12 hours), the inclination angle of the guide plate changes by 0.12 degrees. These change rate data are accumulated to obtain the dynamic evolution of the inclination angle of the guide plate. The curve shows the change of the inclination angle of the guide plate in long-term use. This curve can be used to monitor the status of the guide plate in real time and predict its change trend in a certain period of time in the future. According to the dynamic evolution curve of the guide plate inclination angle and the reference inclination angle of the water inlet guide plate, the change data of the future inclination angle is predicted. First, the reference inclination angle is compared with the dynamic evolution curve. The reference inclination angle is set to 30 degrees. The calculated change rate and accumulated change data are used to obtain the expected change of the guide plate inclination angle in the future. Assuming that after 12 hours, the expected inclination angle of the guide plate changes by 0.1 degrees, it is predicted that the inclination angle of the guide plate will change to 30.1 degrees in the next 12 hours. Based on this prediction, the angle of the guide plate can be adjusted to avoid excessive offset. The future inclination angle change data is input into the surface fitting algorithm, and the data is processed using polynomial fitting (such as quadratic polynomial). The fitting function is set to ,in is the time variable, 、 、 is the fitting coefficient, and the fitting coefficient is calculated by the least square method. Assume that the fitting surface is The surface represents the trend of the inclination angle changing over time. Using this fitting surface, the inclination angle of the guide plate at a certain moment in the future can be accurately predicted. According to the fitted inclination angle change surface, the time series stability analysis is performed. First, the fitting surface is divided into multiple time periods (for example, one time period per hour), and the change trend in each time period is analyzed. If the inclination angle changes greatly in a certain time period, it is marked as a broken segment of the change trend. Assuming that the rate of change of the inclination angle is high between the 5th and 6th hours, this time period is determined to be a broken segment of the change trend. Based on these broken segments, the operation strategy of the guide plate can be adjusted to avoid excessive inclination or uneven flow field. According to the broken segments of the change trend, the structure of the guide plate is mapped. Response area, first determine the pressure distribution area on the guide plate according to the fracture fragment of the change trend of the inclination angle, and calculate the response area of ​​the guide plate by analyzing the pressure distribution and deformation data. Assume that from the 6th hour to the 7th hour of the change trend, the stress point and deformation point of the guide plate are concentrated in the central area of ​​the guide plate. This area should be regarded as the key response area of ​​the guide plate. Through further monitoring and analysis, ensure that this area does not exceed the set structural bearing range. Use the structural response area and particle sedimentation offset data of the guide plate to deduce the deformation state of the flow field. First, based on the known particle sedimentation trajectory offset data, combined with the stress and deformation data of the guide plate, use the fluid dynamics model (such as Navier-Stokes equation) to simulate The change of the flow field, assuming that the sedimentation velocity of the particles in the flow field is 0.2m / s, the degree of flow field deformation is obtained through fluid simulation calculation, the stability of the flow field is analyzed, and the angle of the guide plate is adjusted according to the deformation state to maintain the uniformity and stability of the flow field. The flow field deformation state is gradient analyzed to calculate the flow velocity change rate. Using the velocity field data of the flow field, the flow velocity change rate is set to 0.1m / s². Based on this data, a flow field disturbance intensity map is generated. The intensity of the flow velocity disturbance is obtained through fluid simulation calculation. Combined with the force data of the guide plate, the distribution of the disturbance intensity is generated. This data can be used for further flow field optimization and guide plate adjustment based on the preset critical sedimentation conditions (such as the sediment thickness exceeds 0.5 meters) and the flow field disturbance intensity. Data is used for intensity comparison. Assuming that the sediment load reaches a specific value under critical sedimentation conditions, the abnormal state of sediment imbalance is predicted in combination with the flow field disturbance intensity. If the comparison result shows that the flow field disturbance intensity exceeds the set threshold and the sediment thickness exceeds 0.5 meters, it indicates the occurrence of sediment imbalance. Through this prediction mechanism, measures can be taken in advance to make adjustments. Based on the aforementioned sediment imbalance abnormal state prediction results, sediment imbalance early warning data is generated. Assuming that the early warning condition is that the flow field disturbance intensity exceeds a certain threshold and the sediment thickness reaches the preset condition, the system will automatically generate early warning data and display the early warning graphics through a visual interface, showing potential abnormal areas and providing specific adjustment plans, such as adjusting the water inlet flow rate, changing the inclination angle of the guide plate, etc.

[0118] It is particularly important to compare the preset critical sedimentation conditions with the flow field disturbance intensity, and to predict the abnormal sedimentation imbalance state based on the intensity comparison results, including:

[0119] The preset critical sedimentation conditions are compared with the flow field disturbance intensity point by point, and the spatial distribution correspondence is constructed to determine the intensity ratio of each area;

[0120] Set the disturbance intensity threshold range based on the intensity ratio of each area, and filter out high-risk areas that exceed the threshold based on the disturbance intensity threshold range;

[0121] Conduct spatial cluster analysis of high-risk areas and identify potential sources of imbalance;

[0122] The state evolution of potential imbalance points is simulated according to the flow field disturbance intensity to generate a simulated state evolution path;

[0123] Predict the abnormal state of sedimentation imbalance in the sewage treatment process based on the simulated state evolution path.

[0124] In this embodiment, critical sedimentation conditions are set, including the maximum allowable thickness of sediment, the minimum stable sedimentation velocity, etc. These conditions are obtained through historical data and experimental data of actual sewage treatment facilities. Then, the spatial distribution data of the flow field disturbance intensity are obtained. The calculation of the flow field disturbance intensity is based on the difference between the velocity field and the flow change. For example, the flow field disturbance values ​​of different regions are obtained through a CFD (computational fluid dynamics) model. The flow field disturbance intensity of each region is compared with the critical sedimentation condition by establishing a point-to-point mapping relationship. The intensity ratio of each region is obtained based on the magnitude of the velocity change and the sedimentation rate. The ratio represents the degree of adaptation of the flow field disturbance to the sedimentation conditions. The higher the ratio, the greater the risk of sedimentation imbalance in the region. A threshold range is set. The range is obtained by analyzing historical data and experimental data. The upper and lower limits of the flow field disturbance intensity are usually determined by statistical analysis methods of the velocity field (such as cluster analysis). For example, the threshold value of flow field disturbance intensity is set at 0.5-1.5m / s (meters per second). By comparing the intensity ratio of each area with the set threshold range, we can identify areas that exceed the threshold range. These areas are considered high-risk areas, indicating that the flow field disturbance intensity in these areas will be too high, resulting in excessive sedimentation or imbalance. Through data visualization tools, such as GIS (Geographic Information System) platform, these high-risk areas are marked with different colors, and spatial clustering analysis methods, such as K-means algorithm or DBSCAN (density-based spatial clustering algorithm), are used to perform spatial clustering processing on high-risk areas. The purpose of this process is to identify potential imbalance sources in the area. Through spatial clustering methods, the areas where the flow field disturbance intensity exceeds the standard are clustered and analyzed to find the clustering points in these areas. The clustering points are potential imbalance sources. During the clustering process, certain parameters need to be set, such as the cluster radius (for example, 50 meters) and the minimum number of cluster points (for example, 3 data points). The output of the clustering results is a set of potential imbalance source points, which will serve as the basis for the next state evolution simulation. The state evolution of potential imbalance points is simulated using numerical simulation methods. During the simulation process, the initial conditions of the potential imbalance points are first set, such as sediment thickness, flow field disturbance intensity, sedimentation rate, etc. By establishing a mathematical model based on physical laws (such as the Navier-Stokes equation), combined with the influence of disturbance intensity and sedimentation conditions, the state change process of the imbalance point is simulated. For example, in the simulation, an initial flow field disturbance intensity is set to 1.2 m / s, and the initial sediment thickness is assumed to be 0.3 meters. Numerical calculation tools are used to perform dynamic evolution simulation to calculate the state change path of the point in the future time period (such as within 24 hours). The simulation results provide the changes in sediment thickness and the change trajectory of flow field disturbance intensity at potential imbalance points at different time points, forming a clear state evolution path. Based on the generated simulated state evolution path and combined with the preset critical sedimentation conditions, the abnormal state of sediment imbalance is predicted.If, within a 24-hour period, the sediment thickness at a potential imbalance point in a simulation exceeds a set critical thickness (e.g., 0.5 meters) and the flow field disturbance intensity exceeds 0.8 m / s, that point is considered to be in an abnormal state of sedimentation imbalance. Based on the evolution of the simulation path, the system generates a risk prediction report, indicating the time and area where sedimentation imbalance is likely to occur. It then provides adjustment options, such as increasing or decreasing inflow flow or adjusting pumping intensity, to ensure stable operation of the sewage treatment process. Data from the simulation path is updated and adjusted via a real-time monitoring system.

[0125] Preferably, step S6 includes the following steps:

[0126] Step S61: Decompose the abnormal state of deposition imbalance in time series and extract key time nodes to determine the abnormal occurrence time series;

[0127] Step S62: Back-analyzing abnormal exceeding limit parameters in the initial sewage treatment process model based on the abnormal occurrence time sequence;

[0128] Step S63: performing historical trend analysis on abnormal out-of-limit parameters using preset historical model parameters, and identifying abnormal change patterns; inferring parameter drift characteristics based on the abnormal change patterns;

[0129] Step S64: Correcting the model parameters of the abnormal and out-of-limit parameters based on the parameter drift characteristics, and reconfiguring the parameters of the initial sewage treatment process model based on the corrected model parameters, thereby generating a corrected sewage treatment process model.

[0130] In this example, the time series data of the entire sewage treatment process are decomposed using the empirical mode decomposition (EMD) algorithm based on data such as sediment thickness and flow field disturbance intensity monitored during the sewage treatment process. The EMD algorithm decomposes the raw time series data into multiple intrinsic mode functions (IMFs), each of which reflects signal fluctuations on a different time scale. For abnormal sedimentation imbalance states, the time window of the abnormal signal is identified by extracting IMF components that undergo drastic changes at specific time points. This identifies the critical time points at which the anomaly occurs: the time when sediment thickness exceeds a set critical value or when the flow field disturbance intensity experiences abnormal fluctuations. By comparing the IMF components of multiple time series with historical data, it is possible to accurately extract the core time points that influence sedimentation imbalance. For example, if the monitored flow disturbance intensity suddenly jumps from 0.5 m / s to 1.5 m / s and persists for a certain period, this can be marked as the moment of potential anomaly occurrence. Based on the anomaly occurrence time series extracted in the previous step, the relevant parameters in the sewage treatment process model are traced back and regression analysis is used to evaluate the model input parameters at each time point, focusing on key parameters such as flow rate, flow velocity, and sediment thickness in the treatment tank. By comparing these parameters with historical data, the changing trends of these parameters are analyzed, and which parameters exceed the normal fluctuation range before or after the anomaly time point are identified. For example, if the flow velocity fluctuates from 0.2 m / s to 0.7 m / s and is accompanied by an abnormal increase in sediment thickness, it indicates that the increase in flow velocity has affected sedimentation imbalance. The retrospective process compares the set warning thresholds with the actual parameter changes to derive preliminary abnormal out-of-limit parameters. For example, if the sediment settling rate exceeds the set maximum settling rate during a certain period of time, it is confirmed that the parameter is the root cause of the imbalance. When performing historical trend analysis on abnormal out-of-limit parameters, the sliding window method is used to smooth the historical data. The window size is set to 10 days, and the mean and standard deviation of each parameter are calculated within the window to analyze the fluctuation range of the parameter in the historical data. By comparing the trends in the historical data, it is possible to identify whether there are abnormal change patterns in the parameter values ​​before the abnormality occurs. For example, if the sediment settling rate has been steadily increasing over the past week and reaches the preset critical value when the abnormality occurs, it indicates that the parameter has been drifting continuously. By identifying the drift pattern and combining it with historical data, the drift characteristics of the parameter can be inferred. Usually, the drift pattern can be described by mathematical models such as linear trend and exponential trend. Assuming that a parameter such as sedimentation rate increases linearly, the trend can be described by the equation (in is the parameter value to be predicted, For =0, the initial value of the parameter, is the growth rate, The model then uses the drift characteristics obtained in the previous step to fit the parameters and predict future parameter trends. When correcting for abnormal out-of-limit parameters, the least squares method (LSM) is used to fit the drift parameters. If the sedimentation rate parameter exhibits a linear growth drift trend, the least squares method is used to modify the fitted trend line to obtain new correction parameters. For example, the growth rate a (a) in the drift trend can be adjusted to more closely approximate the observed value. These corrected parameters serve as input to the initial wastewater treatment process model, which is then reconfigured using an optimization algorithm. This algorithm, using either a genetic algorithm (GA) or a particle swarm optimization (PSO), adjusts key model variables, such as flow rate, sediment thickness, and oxygen concentration in the reactor, based on historical data and the corrected parameter values. Ultimately, a new wastewater treatment process model is generated that accurately reflects the actual performance of the wastewater treatment system, ensuring optimal operation throughout the treatment process.

[0131] The present invention also provides a sewage treatment process model parameter correction system for executing the sewage treatment process model parameter correction method described above. The sewage treatment process model parameter correction system includes:

[0132] A data acquisition module is used to collect sensor data of multiple sewage treatment facilities through the sewage treatment sensor network; extract the sewage pipe topology of the sewage treatment sensor network; project the sensor data of multiple sewage facilities into the sewage pipe topology and mark the corresponding location information;

[0133] A model building module is used to build an initial sewage treatment process model based on corresponding location information and multi-point sewage facility sensor data; extract the treatment process sewage data set in the initial sewage treatment process model;

[0134] The environmental simulation module is used to simulate the sewage treatment environment based on the sewage treatment process data set and generate a simulated sewage treatment process environment; simulate virtual flow field data based on the simulated sewage treatment process environment and the sewage treatment process data set;

[0135] The particle tracking module is used to track particle motion based on the wastewater data set and virtual flow field data during treatment and draw particle sedimentation curves. It also performs sedimentation offset mapping fitting based on the preset ideal sedimentation conditions and particle sedimentation curves to generate particle sedimentation trajectory offset data.

[0136] The sedimentation analysis module is used to obtain sedimentation pond morphological parameters; reconstruct the sedimentation pond structure based on the sedimentation pond morphological parameters, and determine the reference inclination angle of the water inlet guide plate based on the sedimentation pond structure; and predict the abnormal state of sedimentation imbalance based on the reference inclination angle of the water inlet guide plate and particle settling offset data;

[0137] The parameter correction module is used to analyze the abnormal and excessive parameters in the initial sewage treatment process model through retrospective analysis of the abnormal state of sedimentation imbalance; based on the abnormal and excessive parameters, the model parameters of the initial sewage treatment process model are corrected to generate a corrected sewage treatment process model.

[0138] The present invention realizes comprehensive monitoring and real-time data acquisition of sewage treatment facilities through the application of data acquisition module, ensuring the accuracy and timeliness of data. The extracted sewage pipe topology structure provides a clear spatial framework for subsequent analysis. Data projection and location information marking enhance the traceability of data and effectively support the basis of model construction. The model construction module generates an initial sewage treatment process model based on multi-point sensor data, providing an accurate description of the treatment process. The extracted sewage data set provides the necessary basis for model optimization. The simulated sewage treatment process environment generated by the environmental simulation module ensures the effective simulation of the actual treatment situation. The simulation of virtual flow field data provides important support for particle motion analysis. The particle tracking module The drawing of sedimentation curves reveals the behavioral characteristics of particles during the treatment process. The generation of sedimentation offset mapping fitting provides a data basis for the optimization of the sedimentation process. The sedimentation pool morphological parameters obtained by the sedimentation analysis module provide a scientific basis for the reconstruction of the sedimentation pool structure. The prediction of the guide plate reference inclination angle can timely identify abnormal sedimentation imbalance states. The parameter correction module clarifies the direction of model parameter adjustment through retrospective analysis of abnormal out-of-limit parameters. The sewage treatment process model after model parameter correction significantly improves the accuracy and applicability of the model, provides reliable theoretical support for the optimized operation of sewage treatment facilities, improves the overall sewage treatment effect and efficiency, and promotes the intelligent and refined development of sewage treatment technology.

[0139] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0140] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for correcting parameters of a sewage treatment process model, characterized in that: The following steps are involved: Step S1: collecting sensor data of multiple sewage treatment facilities through a sewage treatment sensor network; extracting a sewage pipe topology structure of the sewage treatment sensor network; projecting the sensor data of multiple sewage treatment facilities into the sewage pipe topology structure and marking corresponding location information; Step S2: constructing an initial sewage treatment process model based on corresponding location information and sensor data of multiple sewage treatment facilities; extracting a treatment process sewage data set from the initial sewage treatment process model; Step S3: performing sewage treatment environment simulation based on the sewage data set during treatment to generate a simulated sewage treatment process environment; Simulate virtual flow field data based on the simulated sewage treatment process environment and sewage data set; Step S4: tracking particle motion based on the wastewater data set and virtual flow field data during treatment, and drawing a particle sedimentation curve; Based on the preset ideal sedimentation conditions and particle sedimentation curve, sedimentation offset mapping fitting is performed to generate particle sedimentation trajectory offset data; include , analyzing the fluid shear field based on the virtual flow field data, and inferring the shear stress distribution data based on the fluid shear field; Identify wastewater particle parameters in a process wastewater dataset; Combine shear stress distribution data and wastewater particle parameters and establish particle-fluid interaction relationships; Identify particle force data based on particle-fluid interaction relationship and shear stress distribution data; Perform time-domain propulsion simulation based on particle force data to generate simulated propulsion time-domain data; and record particle motion trajectories in the simulated propulsion time-domain data; Extract sedimentation rate characteristics from particle motion trajectories and analyze the time-varying sedimentation rate of particles; Draw a particle sedimentation velocity curve based on the time-varying particle sedimentation velocity; The particle settling rate curve is converted into a displacement representation, thereby obtaining a particle settling displacement curve; Identifying the time relationship in the particle settling displacement curve and drawing the particle settling curve based on the time relationship and the particle settling displacement curve; Step S5: Obtaining sedimentation tank morphological parameters; reconstructing the sedimentation tank structure according to the sedimentation tank morphological parameters, and determining the reference inclination angle of the water inlet guide plate based on the sedimentation tank structure; predicting the abnormal state of sedimentation imbalance based on the reference inclination angle of the water inlet guide plate and the particle settling trajectory offset data; Step S6: analyzing abnormal exceeding limit parameters in the initial sewage treatment process model through retrospective analysis of the abnormal state of sedimentation imbalance; and correcting the model parameters of the initial sewage treatment process model based on the abnormal exceeding limit parameters to generate a corrected sewage treatment process model.

2. The method for correcting parameters of a sewage treatment process model according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Designing the layout of key nodes of the sewage treatment facility to obtain sensor deployment site data; configuring a multi-modal acquisition unit for each sensor deployment site to construct a multi-point composite acquisition unit; Step S12: constructing a sewage treatment sensor network based on the multi-point composite acquisition unit; performing multi-point synchronous sampling based on the sewage treatment sensor network to obtain multi-point sewage treatment facility sensor data, wherein the sampling frequency is set to 1 Hz to 5 Hz, and the sampling time is not less than 10 minutes; Step S13: performing structured decoding processing on the sensor data of multiple sewage treatment facilities to obtain standardized sewage sensor node data; Step S14: Analyze the sewage pipe topology based on the standardized sewage sensor node data and the sewage treatment sensor network, wherein the number of nodes is not less than 20 and the number of connected edges is not less than the number of nodes - 1; Step S15: Projecting the multi-point sewage treatment facility sensor data onto the sewage pipe topology to obtain location information mapping data, with the projection error threshold limited to ≤0.5m; Step S16: Perform site marking processing according to the location information mapping data to obtain corresponding location information of the multi-point sewage treatment facility sensor data.

3. The method for correcting parameters of a sewage treatment process model according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing position data matching on the corresponding position information and the sensor data of multiple sewage treatment facilities, setting the matching tolerance range to ±0.5 meters, and obtaining position information matching data; Step S22: Process segment attribution is performed on the location information matching data. According to the node distribution in the sewage pipe topology, each matching point is assigned to the process segment closest to it. The attribution distance threshold is set to 1.0 meter to obtain process segment allocation data. Step S23: screening the process section allocation data for processing characteristics, selecting data samples with water quality parameters in the range of pH 6.5–8.5, dissolved oxygen greater than 2 mg / L, and COD less than 300 mg / L, to obtain screening sensor data; Step S24: performing time-series splicing on the filtered sensor data, splicing adjacent data in the order of acquisition timestamps, setting the maximum time interval to no more than 10 seconds, and obtaining a spliced ​​sensor data sequence; Step S25: construct an initial sewage treatment process model based on the spliced ​​sensor data sequence; extract the process segment time series fragment data in the initial sewage treatment process model, wherein the minimum time span of the treatment unit is set to 30 seconds; extract the treatment process sewage data set based on the process segment time series fragment data.

4. The method for correcting parameters of a sewage treatment process model according to claim 1, wherein: Step S3 includes the following steps: Step S31: performing multi-zone pressure simulation on the sewage data set during treatment, and recording the pressure response to obtain the unit partial pressure characteristics; Step S32: performing continuous permeation stratification processing on the unit partial pressure characteristics to obtain a layered permeation structure; performing flow channel configuration simulation on the wastewater data set during treatment based on the layered permeation structure to generate a simulated wastewater treatment process environment; Step S33: performing distributed pressure control mapping based on the simulated sewage treatment process environment to obtain unit hydraulic distribution data; and reconstructing the hydraulic time series path based on the unit hydraulic distribution data; Step S34: Perform path logic stitching simulation according to the hydraulic timing path to generate path logic stitching data; and construct a boundary ring structure based on the path logic stitching data; determine the flow boundary conditions based on the boundary ring structure and the hydraulic timing path, and simulate virtual flow field data based on the flow boundary conditions and the sewage data set of the treatment process.

5. The method for correcting parameters of a sewage treatment process model according to claim 1, wherein: The sedimentation offset mapping fitting based on the preset ideal sedimentation condition and the particle sedimentation curve in step S4 includes: Draw an ideal deposition curve based on the preset ideal deposition conditions; Project and compare the ideal sedimentation curve and the particle sedimentation curve, and construct the sedimentation difference matrix; The offset of each particle trajectory is calculated according to the sedimentation difference matrix and integrated into a particle trajectory offset set; Determine the standard particle motion trajectory based on the ideal deposition curve; The particle trajectory offset set is superimposed on the standard particle trajectory, and an offset correction is performed to obtain an offset-corrected trajectory set; Perform spatiotemporal clustering on the offset-corrected trajectory set to obtain trajectory correction point data; Identify key excursion patterns based on trajectory correction point data; The particle sedimentation curve is predicted to be offset through the key offset mode to generate particle sedimentation trajectory offset data.

6. The method for correcting parameters of a sewage treatment process model according to claim 1, characterized in that: Reconstructing the sedimentation tank structure according to the sedimentation tank morphological parameters in step S5 and determining the reference inclination angle of the water inlet guide plate based on the sedimentation tank structure includes: Construct the initial sedimentation tank framework based on the sedimentation tank morphological parameters; Identify the key structural nodes in the initial sedimentation tank framework and connect the nodes based on the key structural nodes to generate the sedimentation tank skeleton network; Identify the geometric skeleton of the inlet area in the sedimentation tank skeleton network; The guide plate position and angle are calculated based on the geometric skeleton of the water inlet area, thereby generating a guide plate parameter set; The reference inclination angle of the water inlet guide plate is determined based on the guide plate parameter set.

7. The method for correcting parameters of a sewage treatment process model according to claim 6, characterized in that: Predicting the abnormal state of sedimentation imbalance based on the reference inclination angle of the water inlet guide plate and the particle settling trajectory deviation data in step S5 includes: The long-term sedimentation process is simulated by using the particle settling trajectory migration data and sediment pool morphology parameters, and the cumulative distribution of sediments is determined based on the long-term sedimentation process; Calculate sediment load distribution based on the cumulative distribution of sediments and generate structural load distribution data; Map the structural load distribution data to the initial sedimentation tank framework and perform finite element stress analysis to generate stress distribution data; Identify the stress concentration points of the guide plate based on stress distribution data and sedimentation pool morphological parameters, and analyze the stress state of the guide plate; Predict the deformation trend of the guide plate according to its stress state; The rate of change of the inclination angle over time is calculated based on the deformation trend of the guide plate and mapped into a dynamic evolution curve of the guide plate inclination angle; The future tilt angle change data is predicted through the dynamic evolution curve of the guide plate tilt angle and the reference tilt angle of the water inlet guide plate; Performing surface fitting processing on the future tilt angle change data to obtain a tilt angle change fitting surface; The fitting surface of the tilt angle change is segmented for temporal stability to generate change trend fracture segments; Based on the fracture fragment mapping of the change trend, the corresponding deflector structure response area; The flow field deformation state is deduced based on the corresponding guide plate structure response area and particle settling trajectory deviation data; Perform gradient analysis on the deformation state of the flow field, calculate the corresponding velocity change rate of the guide plate area, and generate the flow field disturbance intensity; An intensity comparison is performed based on the preset critical sedimentation conditions and the flow field disturbance intensity, and the abnormal state of sedimentation imbalance is predicted based on the intensity comparison results.

8. The method for correcting parameters of a sewage treatment process model according to claim 1, wherein: Step S6 includes the following steps: Step S61: Decompose the abnormal state of deposition imbalance in time series and extract key time nodes to determine the abnormal occurrence time series; Step S62: Back-analyzing abnormal exceeding limit parameters in the initial sewage treatment process model based on the abnormal occurrence time sequence; Step S63: performing historical trend analysis on abnormal out-of-limit parameters using preset historical model parameters, and identifying abnormal change patterns; inferring parameter drift characteristics based on the abnormal change patterns; Step S64: Correcting the model parameters of the abnormal and out-of-limit parameters based on the parameter drift characteristics, and reconfiguring the parameters of the initial sewage treatment process model based on the corrected model parameters, thereby generating a corrected sewage treatment process model.

9. A sewage treatment process model parameter correction system, characterized in that: For executing the sewage treatment process model parameter correction method according to claim 1, the sewage treatment process model parameter correction system comprises: A data acquisition module is used to collect sensor data of multiple sewage treatment facilities through the sewage treatment sensor network; extract the sewage pipe topology of the sewage treatment sensor network; project the sensor data of multiple sewage treatment facilities into the sewage pipe topology and mark the corresponding location information; A model building module is used to build an initial sewage treatment process model based on corresponding location information and sensor data of multiple sewage treatment facilities; extract the treatment process sewage data set in the initial sewage treatment process model; The environmental simulation module is used to simulate the sewage treatment environment based on the sewage treatment process data set and generate a simulated sewage treatment process environment; simulate virtual flow field data based on the simulated sewage treatment process environment and the sewage treatment process data set; The particle tracking module is used to track particle motion based on the wastewater data set and virtual flow field data during treatment and draw particle sedimentation curves. It also performs sedimentation offset mapping fitting based on the preset ideal sedimentation conditions and particle sedimentation curves to generate particle sedimentation trajectory offset data. The sedimentation analysis module is used to obtain sedimentation pond morphological parameters; reconstruct the sedimentation pond structure based on the sedimentation pond morphological parameters, and determine the reference inclination angle of the water inlet guide plate based on the sedimentation pond structure; and predict the abnormal state of sedimentation imbalance based on the reference inclination angle of the water inlet guide plate and the particle settling trajectory deviation data; The parameter correction module is used to analyze the abnormal and excessive parameters in the initial sewage treatment process model through retrospective analysis of the abnormal state of sedimentation imbalance; based on the abnormal and excessive parameters, the model parameters of the initial sewage treatment process model are corrected to generate a corrected sewage treatment process model.

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