Digital management method for rural construction based on digital twinning

Abstract

The invention relates to the technical field of data processing, and discloses a digital country construction management method based on digital twinning, which comprises the following steps: collecting time sequence data returned by a sensor network, processing the time sequence data to obtain a structured data set, mapping the structured data set to a corresponding point location of a three-dimensional twinning mirror image, and establishing spatial topological association; judging an abnormal level and quantifying influence range data; positioning an abnormal emission source by using a reverse tracing algorithm and acquiring abnormal emission information; and generating a structured evaluation report, and generating a hierarchical collaborative management and control instruction based on the evaluation report. By implementing the method and the system, the problem that the actual demand of precise, efficient and low-cost rural sewage management and control cannot be met due to the defects of insufficient suitability, low monitoring precision, weak data processing and association capability, low abnormal traceability and management and control efficiency, lack of a closed-loop management and control mechanism and the like existing in the current rural distributed domestic sewage management and control technology is solved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a digital management method for rural construction based on digital twins. Background Technology

[0002] Currently, rural decentralized domestic sewage management technology has mainly gone through three development stages: manual inspection, traditional monitoring equipment management, and simplified information-based management. Early manual inspection was not only time-consuming and labor-intensive, with slow response times, but also prone to data subjectivity and large errors, making it impossible to promptly detect and handle abnormal emissions, especially unsuitable for rural areas with complex terrain and scattered villages. To address the shortcomings of manual inspection, traditional monitoring equipment management emerged, but this model has significant drawbacks: first, sensor deployment lacks differentiated design; second, data transmission and processing mechanisms are rudimentary, making it difficult to guarantee data quality; and third, it fails to establish a connection between data and the physical environment, resulting in isolated monitoring data that cannot intuitively reflect the sewage flow path and impact range, greatly hindering anomaly tracing. Simplified information-based management systems still have many core limitations: on the one hand, the lack of lightweight digital carriers adapted to rural scenarios makes it difficult for management personnel to accurately grasp the overall sewage flow situation; on the other hand, anomaly judgment often uses fixed threshold modes, leading to high rates of false positives and false negatives; simultaneously, anomaly tracing relies on manual experience deduction, resulting in low management efficiency and serious resource waste. Digital twin technology, as an advanced technology that enables real-time linkage between physical entities and virtual mirrors, has been successfully applied in fields such as urban pipe networks and industrial production, providing an effective solution for the precise control of complex systems. However, the application of existing digital twin technology in rural sewage management is still in the exploratory stage. Existing attempts often directly copy the digital twin architecture of urban pipe networks, resulting in problems such as overly complex models, high deployment costs, and high operational difficulties. These attempts have not fully considered the actual needs of rural areas for low-cost operation and maintenance and the shortage of technical personnel. For example, the use of high-precision terrain modeling increases the cost of model construction and maintenance, and complex algorithm operations require professional technical personnel to be on-site, making it difficult to promote and apply in rural areas.

[0003] In summary, current decentralized rural domestic sewage management technologies generally suffer from shortcomings such as insufficient adaptability, low monitoring accuracy, weak data processing and correlation capabilities, low efficiency in anomaly tracing and control, and lack of closed-loop management mechanisms, which fail to meet the actual needs of precise, efficient, and low-cost rural sewage management. Summary of the Invention

[0004] In view of this, the present invention provides a digital management method for rural construction based on digital twins to solve the problems that current decentralized rural domestic sewage management technologies generally suffer from, such as insufficient adaptability, low monitoring accuracy, weak data processing and correlation capabilities, low efficiency in anomaly tracing and control, and lack of closed-loop management mechanisms, which cannot meet the actual needs of precise, efficient and low-cost management of rural sewage.

[0005] This invention provides a digital management method for rural construction based on digital twins. The method includes: collecting time-series data returned by a sensor network; processing the time-series data to obtain a structured dataset; classifying and deploying the sensor network based on a deployment strategy; constructing a three-dimensional twin mirror image; mapping the structured dataset to corresponding points in the three-dimensional twin mirror image; establishing spatial topological relationships; using a time-series segmentation analysis algorithm and an emission prediction model to obtain a dynamic threshold for anomaly detection based on the structured dataset and historical data from the same period; determining the anomaly level and quantifying the impact range based on the structured dataset, spatial topological relationships, and the dynamic threshold for anomaly detection; using a reverse tracing algorithm to locate abnormal emission sources and obtain abnormal emission information based on spatial topological relationships, anomaly level, and quantified impact range data; generating a structured assessment report based on the abnormal emission information; and generating hierarchical collaborative control instructions based on the assessment report.

[0006] The digital twin-based rural construction digital management method provided in this embodiment firstly involves classifying and deploying a sensor network based on a deployment strategy. This collects time-series data from various regions and processes it to obtain a structured dataset, achieving differentiated adaptation and standardized preprocessing of rural sewage control data collection. Sensors of appropriate types are matched to the emission characteristics of different rural areas, ensuring the accuracy and stability of data collection. Systematic processing techniques such as outlier removal, unit standardization, and time stamp standardization effectively improve data quality and eliminate noise and format differences in the original data. Secondly, by constructing a three-dimensional twin mirror, the structured dataset is mapped to corresponding points in the mirror, establishing spatial topological relationships. This achieves precise mapping and real-time linkage between the physical control scenario and the virtual digital model. The three-dimensional twin mirror construction technology can accurately replicate the spatial attributes and physical characteristics of core elements such as emission sources, pipe networks, and treatment facilities. The data mapping technology achieves a one-to-one correspondence between structured data and mirror points, while the spatial topological relationship construction technology forms a computable and traceable spatial relationship model by sorting out the connectivity and influence paths between various elements. This provides a visual and interactive technical carrier for subsequent spatial-dimensional algorithm analysis and control decisions. Then, by employing a time-series segmentation analysis algorithm and an emission prediction model, combined with structured datasets and historical data from the same period, a dynamic threshold for anomaly detection is obtained. This technically overcomes the limitations of traditional fixed threshold detection, achieving intelligent and dynamic anomaly detection. The time-series segmentation analysis algorithm can accurately capture the temporal and periodic characteristics of wastewater discharge, providing precise temporal basis for threshold division. The emission prediction model learns the correlation patterns between historical data from the same period and real-time structured data to predict wastewater discharge trends. The two work together to generate a dynamic threshold through residual feedback iteration, enabling the anomaly detection standard to adapt to the dynamic changes in rural wastewater discharge in real time. This improves the accuracy of anomaly identification at the algorithmic level and effectively reduces the technical risks of misjudgment and missed judgment. Furthermore, by combining structured datasets, spatial topological correlations, and dynamic thresholds for anomaly detection to determine anomaly levels and quantify the scope of impact, a multi-dimensional and accurate quantitative assessment of anomalies is achieved. By integrating the dual technological advantages of data-driven and spatial modeling, the system achieves scientific classification of anomaly levels through multi-dimensional data fusion analysis. It accurately defines the spatial boundaries and extent of anomaly impacts using spatial topological correlation models and quantitative analysis techniques. The generated quantitative data has clear technical indicator significance, providing precise technical basis for subsequent management resource allocation and management strategy formulation, and improving the pertinence and scientific nature of anomaly handling.Furthermore, by employing a reverse tracing algorithm, abnormal emission sources are located and abnormal emission information is obtained based on spatial topological correlation, anomaly level, and quantified impact range data. Utilizing path guidance provided by the spatial topological correlation model, a source tracing weight matrix is ​​constructed by combining anomaly level and impact range data, rapidly narrowing the tracing scope and improving efficiency. Simultaneously, multi-source data cross-validation technology further ensures tracing accuracy. The final integrated abnormal emission information includes precise spatial coordinates, exceedance types, and other multi-dimensional technical parameters, providing precise technical targets for anomaly response. Finally, by generating a structured assessment report based on the abnormal emission information, and combining this report with hierarchical collaborative control instructions, a closed-loop intelligent management system for rural sewage anomalies is achieved. The structured assessment report generation technology systematically integrates technical data from multiple stages, including anomaly assessment and source tracing, forming standardized technical documents. The hierarchical collaborative control instruction generation technology achieves precise matching of control levels and measures based on the assessment results. Through equipment linkage control and dynamic adjustment of monitoring frequency, it enables precise allocation of control resources and multi-stage collaborative linkage, constructing a technical closed loop. This effectively improves the technical efficiency and control effect of anomaly response, promoting the transformation of rural sewage management from traditional experience-based to modern technology-driven approaches. By implementing this invention, we can solve the problems that current decentralized rural domestic sewage management technologies generally suffer from, such as insufficient adaptability, low monitoring accuracy, weak data processing and correlation capabilities, low efficiency in anomaly tracing and control, and lack of closed-loop management mechanisms. These shortcomings prevent us from meeting the actual needs of precise, efficient, and low-cost rural sewage management.

[0007] In one alternative implementation, the deployment strategy is based on deployment zones, which include concentrated farmer areas, independent farmer areas, public facility areas, key pipeline node areas, and treatment facility areas. The deployment strategy includes: deploying ultrasonic flow meters and COD / pH dual-parameter sensors at the septic tank outlets in concentrated farmer areas; deploying passive flow monitoring sensors at the courtyard drainage outlets in independent farmer areas; deploying ammonia nitrogen / COD dual-parameter monitoring sensors and anomaly marker integrated monitoring sensors at the outlets of public facility areas; deploying integrated water level and flow velocity sensors in key pipeline node areas; and deploying flow and pH sensors interlocked with inlet valves at the inlet and outlet of treatment facility areas.

[0008] In one optional implementation, time-series data returned by the sensor network is collected, and the time-series data is processed to obtain a structured dataset. This includes: collecting time-series data from each deployment partition, including wastewater discharge flow data, wastewater COD concentration data, wastewater pH value data, wastewater ammonia nitrogen concentration data, pipeline water level data, pipeline flow velocity data, inlet and outlet flow data of the treatment facility, and inlet and outlet pH value data of the treatment facility; transmitting the time-series data to a multi-source data fusion gateway using a dual-mode approach of timed reporting and anomaly triggering; and using the multi-source data fusion gateway to sequentially perform outlier removal, missing value imputation, data unit unification, and timestamp standardization on the time-series data, and integrating them to generate a structured dataset.

[0009] In one optional implementation, the construction process of the three-dimensional twin mirror includes: extracting prior feature information on the distribution of emission sources, pipeline routing, coordinates of treatment facilities, and the range of sensitive water bodies within each deployment zone, and establishing a feature information list; acquiring UAV low-altitude photography image data and spatial location data, and using a point cloud fusion algorithm to generate an initial three-dimensional model based on the UAV low-altitude photography image data and spatial location data; and adding sensor number association identifiers to the emission point coordinates, adding hydraulic transmission path annotations to the pipeline routing and burial depth, and adding equipment operation status association interfaces to the treatment facility locations to obtain the three-dimensional twin mirror.

[0010] In one optional implementation, the structured dataset is mapped to corresponding points in the 3D twin mirror to establish spatial topological associations. This includes: establishing an association between the structured dataset and the 3D twin mirror based on sensor number association identifiers, hydraulic transmission path annotations, and equipment operation status association interfaces; using a data mapping engine to map the structured dataset to corresponding points in the 3D twin mirror based on the association relationship; and generating a spatial association analysis algorithm based on the core elements in the 3D twin mirror to construct spatial topological associations. The spatial topological associations characterize the associations between emission sources, pipeline segments, pipeline nodes, treatment facilities, and surrounding water bodies.

[0011] In one optional implementation, a dynamic threshold for anomaly determination is obtained based on a structured dataset and historical data from the same period, using a time-series segmented analysis algorithm and an emission prediction model. This includes: acquiring historical data from the same period, which is time-series data of rural sewage discharge in the same season and time period over the past three years; segmenting the structured dataset and historical data using a time-series segmented analysis algorithm to identify peak sewage discharge characteristics and generate a time-specific normal threshold; inputting the structured dataset and historical data into the emission prediction model to obtain predicted sewage discharge data for a preset future time period; calculating the residual vector between the predicted sewage discharge data and real-time monitoring data, and iteratively optimizing the time-specific normal threshold through residual feedback to obtain the dynamic threshold for anomaly determination.

[0012] In one optional implementation, the anomaly level and the quantified impact range data are determined based on the structured dataset, spatial topological correlation, and dynamic threshold for anomaly determination, including: determining the anomaly level based on real-time monitoring data in the structured dataset, dynamic threshold for anomaly determination, and preset standards; and generating quantified impact range data based on the structured data and spatial topological correlation using a spatial correlation analysis algorithm.

[0013] In one optional implementation, a reverse tracing algorithm is used to locate abnormal emission sources and obtain abnormal emission information based on spatial topological correlation, anomaly level, and quantified impact range data. This includes: constructing an anomaly source tracing data matrix containing spatial node weights and anomaly impact coefficients based on a spatial topological correlation model, anomaly level determination results, and quantified impact range data; importing the anomaly source tracing data matrix into the reverse tracing algorithm, using the boundary nodes of the quantified impact range as the starting point, traversing the hydraulic transmission path of the pipeline network in reverse, and filtering out candidate abnormal emission source locations matching the anomaly level; calculating the anomaly diffusion time based on the anomaly diffusion area, pipeline hydraulic transmission velocity, and fluid dynamic characteristics in the quantified impact range data, and determining the abnormal emission time window for the candidate abnormal emission source locations; cross-validating the candidate abnormal emission source locations using the acquisition time difference based on the abnormal emission time window to locate the abnormal emission source; obtaining the precise spatial coordinates, anomaly level, abnormal emission time window, anomaly index exceedance type, and exceedance magnitude of the abnormal emission source, and integrating these to obtain abnormal emission information.

[0014] In one optional implementation, a structured assessment report is generated based on abnormal emission information, and a tiered collaborative control instruction is generated based on the assessment report. This includes: extracting core data from the abnormal emission information and generating a structured assessment report in a preset format that includes an anomaly overview, source tracing results, impact range, and disposal recommendations; matching the target tiered control standard based on the anomaly level and disposal recommendations to determine the control priority and the scope of linked equipment; and generating a tiered collaborative control instruction based on the target tiered control standard, wherein the control instruction includes equipment action instructions, monitoring frequency adjustment instructions, and information push instructions.

[0015] In one optional implementation, the method further includes: issuing hierarchical collaborative control instructions to corresponding linked devices and control terminals, collecting device execution status data and on-site monitoring data in real time to obtain control feedback data; verifying whether the anomaly has been eliminated based on the control feedback data and the dynamic threshold for anomaly determination; if the anomaly has been eliminated, recording the control process and updating it to a three-dimensional twin mirror to form a control file; if the anomaly has not been eliminated, upgrading the control level based on the control feedback data and generating feedback hierarchical collaborative control instructions until the anomaly is eliminated. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a digital management method for rural construction based on digital twins according to an embodiment of the present invention. Detailed Implementation

[0018] With the deepening of the rural revitalization strategy, the improvement of rural living environment has become a key task, and the effective management of decentralized domestic sewage is a crucial link in improving the rural ecological environment. Unlike the centralized sewage treatment model in cities, rural domestic sewage has significant characteristics such as dispersed discharge points, large fluctuations in discharge volume, simple but uneven spatial and temporal distribution of water quality components, and a lack of operation and maintenance funds and technical personnel. This makes it difficult to directly adapt mature urban sewage management technologies to rural scenarios, resulting in long-term problems such as low efficiency, high costs, and unstable effects in rural sewage management.

[0019] Currently, rural decentralized domestic sewage management technology has mainly gone through three development stages: manual inspection, traditional monitoring equipment management, and simplified information-based management. Early manual inspection relied on maintenance personnel periodically inspecting the status of discharge outlets, pipe networks, and treatment facilities, collecting water quality and quantity data using portable instruments. This model was not only time-consuming and labor-intensive, with slow response times, but also prone to data subjectivity and large errors, failing to achieve timely detection and handling of abnormal discharges, and was particularly unsuitable for rural areas with complex terrain and scattered villages. To address the shortcomings of manual inspection, the traditional monitoring equipment management model emerged, achieving automatic data collection by deploying single-type sensors at key discharge points, such as flow sensors in areas with concentrated farm households and water quality sensors at the outlet of treatment facilities. However, this model has significant drawbacks: First, the sensor deployment lacks differentiated design, failing to select suitable equipment based on the emission characteristics of different areas such as concentrated farmer areas, independent farmer areas, and public facility areas. This results in distorted monitoring data or high equipment failure rates in some areas. For example, in independent farmer areas, the power supply is unstable, causing frequent shutdowns of conventional active sensors. Second, the data transmission and processing mechanism is rudimentary, mostly using a single timed reporting mode. This either increases transmission costs due to excessively high reporting frequency or misses opportunities to respond to anomalies due to excessively low frequency. Furthermore, there is a lack of effective data cleaning and standardization, making it difficult to guarantee data quality. Third, the data is not linked to the physical scene, resulting in isolated monitoring data that cannot intuitively reflect the wastewater flow path and impact range, making it extremely difficult to trace the source of anomalies.

[0020] In recent years, some regions have begun to explore the introduction of simplified information-based management and control systems. These systems aggregate data from multiple sensors through data gateways and utilize basic data analysis modules to support management and control decisions. However, such systems still suffer from several key limitations: Firstly, they lack lightweight digital carriers adapted to rural scenarios, failing to achieve spatial visualization of core elements such as pipe networks, emission sources, and treatment facilities, making it difficult for management personnel to accurately grasp the overall sewage flow situation. Secondly, anomaly detection often relies on fixed threshold models, failing to consider the changing patterns of rural sewage discharge with seasons, holidays, and farmers' daily routines, leading to high rates of false positives and false negatives. Furthermore, anomaly tracing relies on manual experience and lacks precise tracing algorithms based on spatial topology, making it difficult to quickly locate abnormal emission sources. Moreover, the generation and execution of management instructions lack a closed-loop feedback mechanism, hindering dynamic adjustments to management strategies based on treatment effectiveness, resulting in low management efficiency and significant resource waste. Digital twin technology, as an advanced technology enabling real-time linkage between physical entities and virtual mirrors, has been successfully applied in urban pipe networks, industrial production, and other fields, providing an effective solution for the precise management and control of complex systems. However, the application of existing digital twin technology in rural sewage management is still in the exploratory stage. Existing attempts often directly copy the digital twin architecture of urban pipe networks, resulting in problems such as overly complex models, high deployment costs, and high operational difficulties. They have not fully considered the actual needs of low-cost operation and maintenance and the shortage of technical personnel in rural areas. For example, the use of high-precision terrain modeling increases the cost of model construction and maintenance, and complex algorithm operations require professional technical personnel to be on duty, making it difficult to promote and apply in rural areas.

[0021] In summary, current decentralized rural domestic sewage management technologies generally suffer from shortcomings such as insufficient adaptability, low monitoring accuracy, weak data processing and correlation capabilities, low efficiency in anomaly tracing and control, and lack of closed-loop management mechanisms, which fail to meet the actual needs of precise, efficient, and low-cost rural sewage management.

[0022] The digital twin-based rural construction digital management method provided in this embodiment firstly involves classifying and deploying a sensor network based on a deployment strategy. This collects time-series data from various regions and processes it to obtain a structured dataset, achieving differentiated adaptation and standardized preprocessing of rural sewage control data collection. Sensors of appropriate types are matched to the emission characteristics of different rural areas, ensuring the accuracy and stability of data collection. Systematic processing techniques such as outlier removal, unit standardization, and time stamp standardization effectively improve data quality and eliminate noise and format differences in the original data. Secondly, by constructing a three-dimensional twin mirror, the structured dataset is mapped to corresponding points in the mirror, establishing spatial topological relationships. This achieves precise mapping and real-time linkage between the physical control scenario and the virtual digital model. The three-dimensional twin mirror construction technology can accurately replicate the spatial attributes and physical characteristics of core elements such as emission sources, pipe networks, and treatment facilities. The data mapping technology achieves a one-to-one correspondence between structured data and mirror points, while the spatial topological relationship construction technology forms a computable and traceable spatial relationship model by sorting out the connectivity and influence paths between various elements. This provides a visual and interactive technical carrier for subsequent spatial-dimensional algorithm analysis and control decisions. Then, by employing a time-series segmentation analysis algorithm and an emission prediction model, combined with structured datasets and historical data from the same period, a dynamic threshold for anomaly detection is obtained. This technically overcomes the limitations of traditional fixed threshold detection, achieving intelligent and dynamic anomaly detection. The time-series segmentation analysis algorithm can accurately capture the temporal and periodic characteristics of wastewater discharge, providing precise temporal basis for threshold division. The emission prediction model learns the correlation patterns between historical data from the same period and real-time structured data to predict wastewater discharge trends. The two work together to generate a dynamic threshold through residual feedback iteration, enabling the anomaly detection standard to adapt to the dynamic changes in rural wastewater discharge in real time. This improves the accuracy of anomaly identification at the algorithmic level and effectively reduces the technical risks of misjudgment and missed judgment. Furthermore, by combining structured datasets, spatial topological correlations, and dynamic thresholds for anomaly detection to determine anomaly levels and quantify the scope of impact, a multi-dimensional and accurate quantitative assessment of anomalies is achieved. By integrating the dual technological advantages of data-driven and spatial modeling, the system achieves scientific classification of anomaly levels through multi-dimensional data fusion analysis. It accurately defines the spatial boundaries and extent of anomaly impacts using spatial topological correlation models and quantitative analysis techniques. The generated quantitative data has clear technical indicator significance, providing precise technical basis for subsequent management resource allocation and management strategy formulation, and improving the pertinence and scientific nature of anomaly handling.Furthermore, by employing a reverse tracing algorithm, abnormal emission sources are located and abnormal emission information is obtained based on spatial topological correlation, anomaly level, and quantified impact range data. Utilizing path guidance provided by the spatial topological correlation model, a source tracing weight matrix is ​​constructed by combining anomaly level and impact range data, rapidly narrowing the tracing scope and improving efficiency. Simultaneously, multi-source data cross-validation technology further ensures tracing accuracy. The final integrated abnormal emission information includes precise spatial coordinates, exceedance types, and other multi-dimensional technical parameters, providing precise technical targets for anomaly response. Finally, by generating a structured assessment report based on the abnormal emission information, and combining this report with hierarchical collaborative control instructions, a closed-loop intelligent management system for rural sewage anomalies is achieved. The structured assessment report generation technology systematically integrates technical data from multiple stages, including anomaly assessment and source tracing, forming standardized technical documents. The hierarchical collaborative control instruction generation technology achieves precise matching of control levels and measures based on the assessment results. Through equipment linkage control and dynamic adjustment of monitoring frequency, it enables precise allocation of control resources and multi-stage collaborative linkage, constructing a technical closed loop. This effectively improves the technical efficiency and control effect of anomaly response, promoting the transformation of rural sewage management from traditional experience-based to modern technology-driven approaches. By implementing this invention, we can solve the problems that current decentralized rural domestic sewage management technologies generally suffer from, such as insufficient adaptability, low monitoring accuracy, weak data processing and correlation capabilities, low efficiency in anomaly tracing and control, and lack of closed-loop management mechanisms. These shortcomings prevent us from meeting the actual needs of precise, efficient, and low-cost rural sewage management.

[0023] According to an embodiment of the present invention, a method for digital management of rural construction based on digital twins is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0024] This embodiment provides a digital management method for rural construction based on digital twins. Figure 1 This is a flowchart of a digital management method for rural construction based on digital twins according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Collect time-series data returned by the sensor network, process the time-series data to obtain a structured dataset, and deploy the sensor network according to the deployment strategy.

[0025] In some alternative embodiments, the deployment strategy is based on deployment partitions, which include concentrated farmer areas, independent farmer areas, public facility areas, key pipeline node areas, and treatment facility areas. The deployment strategy includes: deploying ultrasonic flow meters and COD / pH dual-parameter sensors at the septic tank outlets in concentrated farmer areas; deploying passive flow monitoring sensors at the courtyard drain outlets in independent farmer areas; deploying ammonia nitrogen / COD dual-parameter monitoring sensors and anomaly marker integrated monitoring sensors at the outlets of public facility areas; deploying integrated water level and flow velocity sensors in key pipeline node areas; and deploying flow and pH sensors interlocked with inlet valves at the inlet and outlet of treatment facility areas.

[0026] Furthermore, the deployment zones are differentiated control areas divided according to the distribution of rural sewage discharge sources, facility functions, and pipeline network layout; concentrated farmer areas refer to areas where farmers live relatively densely, sewage discharge is large, and discharge is relatively concentrated; independent farmer areas refer to areas where farmers live scattered and sewage discharge points are relatively independent; public facility areas refer to areas where public buildings such as schools, health clinics, and cultural activity centers are located in rural areas; key pipeline network nodes refer to areas where the main and branch lines of the rural sewage pipeline network intersect, where the pipeline network turns, and where the slope changes, etc., play a key role in sewage transmission; treatment facility areas refer to areas where centralized rural sewage treatment equipment is located, including key locations such as the inlet and outlet of the treatment equipment. The specific implementation of the deployment strategy is as follows: ultrasonic flow meters and COD / pH combined sensors are installed at the outlets of septic tanks in concentrated farmer areas; passive flow monitoring sensors are installed at the drainage outlets of courtyards in independent farmer areas; ammonia nitrogen and COD dual-parameter monitoring sensors and anomaly marker integrated monitoring sensors are installed at the outlets of public facility areas; water level and flow velocity integrated sensors are installed at key nodes in the pipeline network; and flow and pH sensors interlocked with the inlet valves are installed at the inlet and outlet of treatment facility areas. The placement of each sensor must be precisely aligned with the corresponding sewage discharge outlet or key monitoring point in the area to ensure the effectiveness of data collection.

[0027] Specifically, step S101 includes: Step S1011: Collect time-series data in each deployment partition. The time-series data includes wastewater discharge flow data, wastewater COD concentration data, wastewater pH value data, wastewater ammonia nitrogen concentration data, pipeline water level data, pipeline flow velocity data, inlet and outlet flow data of treatment facilities, and inlet and outlet pH value data of treatment facilities.

[0028] Furthermore, wastewater discharge flow rate data reflects the amount of wastewater discharged per unit time; wastewater COD concentration data reflects the chemical oxygen demand in wastewater, used to characterize the pollution level of reducing substances in wastewater; wastewater pH value data reflects the acidity or alkalinity of wastewater; wastewater ammonia nitrogen concentration data reflects the ammonia nitrogen content in wastewater, used to characterize the pollution level of nitrogenous pollutants in wastewater; pipeline water level data reflects the wastewater level height within the pipeline network; pipeline flow velocity data reflects the flow speed of wastewater within the pipeline network; inlet and outlet flow rate data of the treatment facility reflect the amount of wastewater entering and leaving the treatment facility, respectively; and inlet and outlet pH value data of the treatment facility reflect the acidity or alkalinity of wastewater entering and leaving the treatment facility, respectively. During implementation, all types of sensors deployed in each deployment zone are activated. The sensors capture corresponding time-series data in real time. The initial capture and storage of raw data are completed through the sensor's built-in data acquisition module, ensuring that all types of time-series data in each deployment zone are completely collected without any omissions or parameters.

[0029] Step S1012: The time-series data is transmitted to the multi-source data fusion gateway using a dual-mode approach of timed reporting and exception triggering.

[0030] Furthermore, the timed reporting mode refers to a transmission mode where a fixed time interval is set, and the sensor periodically uploads the collected time-series data to the designated receiving device at that time interval. The anomaly triggering mode refers to a transmission mode where an initial threshold is preset for the sensor's data collection. When the time-series data captured by the sensor exceeds this initial threshold, the data upload process is automatically triggered, and the abnormal data is uploaded to the designated receiving device in real time. The multi-source data fusion gateway is a core device with the functions of receiving, parsing, and initially integrating multiple types of data. It is used to receive time-series data from different sensors and different deployment partitions, and to provide data aggregation support for subsequent data processing. In implementation, the time interval for timed reporting and the initial threshold for anomaly triggering are first configured for each sensor. Then, the dual-mode data transmission process is started. Under normal operating conditions, each sensor uploads time-series data to the multi-source data fusion gateway at the set time interval. When the time-series data collected by a certain sensor exceeds the preset initial threshold, the anomaly upload mechanism is immediately triggered, and the abnormal time-series data is uploaded to the multi-source data fusion gateway first, ensuring the periodic aggregation of normal data and the real-time reporting of abnormal data.

[0031] Step S1013: The multi-source data fusion gateway is used to sequentially process the time series data by removing outliers, filling in missing values, unifying data units, and standardizing timestamps, and then integrates them to generate a structured dataset.

[0032] Furthermore, outlier removal refers to the process of identifying and removing invalid data in time series data that deviates from the normal data range due to factors such as sensor failure and transmission interference; missing value imputation refers to the process of reasonably supplementing missing data in time series data caused by transmission interruption, temporary sensor failure, etc., to ensure data continuity; data unit unification refers to the process of adjusting the measurement units of the same type of time series data collected by different sensors to a unified standard to eliminate the impact of unit differences on subsequent analysis; and timestamp standardization refers to the process of adjusting the timestamps corresponding to the time series data uploaded by each sensor to a unified time format and time base to ensure the consistency of the data time dimension. During implementation, the multi-source data fusion gateway first performs outlier removal processing on the received time-series data, filtering out invalid data and removing it according to preset outlier identification rules; then, it performs missing value imputation processing on the data after outlier removal, using imputation methods that conform to the data change patterns to supplement missing data; subsequently, it performs unified processing on the data units, converting similar data from different units into preset standard units; finally, it performs timestamp standardization processing, unifying the time format and time base of each data. After completing the above series of processing, the data is integrated and sorted, ultimately generating a structured dataset.

[0033] Step S102: Construct a three-dimensional twin mirror image, map the structured dataset to the corresponding points in the three-dimensional twin mirror image, and establish spatial topological associations.

[0034] Specifically, step S102 includes: Step S1021: Extract prior feature information on the distribution of emission sources, pipeline routes, coordinates of treatment facilities, and the range of sensitive water bodies within each deployment zone, and establish a feature information list.

[0035] Furthermore, prior feature information refers to the basic feature data about the core elements of the control scenario obtained through preliminary surveys and data collection before the 3D mirror construction; emission source distribution refers to the specific spatial distribution of sewage discharge points within each deployment zone; pipeline network direction refers to the spatial layout information such as the extension direction and connection relationship of the sewage pipeline network; treatment facility coordinates refer to the specific location coordinates of sewage treatment equipment in physical space; sensitive water body range refers to the spatial range of rivers, ponds, groundwater recharge areas, and other water bodies in rural areas that are relatively sensitive to pollution; and the feature information list refers to a standardized information set formed by organizing the extracted prior feature information according to a preset format. Through on-site surveys combined with existing planning data, property rights data, etc., prior feature information on emission source distribution, pipeline network direction, treatment facility coordinates, and sensitive water body range within each deployment zone is extracted one by one. The extracted information is verified for authenticity and completeness to ensure that the feature information of each element is accurate. Subsequently, the verified information is classified and entered into a unified format to establish a complete feature information list.

[0036] Step S1022: Acquire UAV low-altitude photography image data and spatial location data, and use point cloud fusion algorithm to generate an initial 3D model based on the UAV low-altitude photography image data and spatial location data.

[0037] Furthermore, UAV low-altitude photography image data refers to image data reflecting the terrain and land feature characteristics of rural sewage management areas, captured by UAVs equipped with photographic equipment during low-altitude flight; spatial location data refers to coordinate data used to characterize the physical spatial location of each pixel in the photographic image; the initial 3D model refers to a preliminary 3D model with the basic spatial structure of the rural sewage management area, constructed based on the image data and spatial location data. First, the UAV's low-altitude flight path is planned to ensure complete coverage of all deployment zones and sensitive water bodies, and the flight altitude is controlled to ensure image clarity. Then, the UAV is launched for low-altitude photography, simultaneously collecting corresponding spatial location data. The acquired UAV low-altitude photography image data and spatial location data are input into the data processing module. Point cloud fusion algorithms are used to preprocess the data, perform point cloud registration, and fuse the points to eliminate noise and biases. Based on the fused point cloud data, an initial 3D model is generated.

[0038] Step S1023: On the initial 3D model, add sensor number association identifiers to the coordinates of the emission points, add hydraulic transmission path labels to the pipeline route and burial depth, and add equipment operation status association interfaces to the location of the treatment facilities to obtain a 3D twin mirror.

[0039] Furthermore, the emission point coordinates refer to the specific coordinate positions of the sewage emission points within each deployment zone in the 3D model; the sensor number association identifier refers to the identification information used to establish a one-to-one correspondence between emission points and corresponding deployed sensor numbers; the hydraulic transmission path annotation refers to the annotation information marked on the pipeline network model to characterize the flow direction and transmission path of sewage within the pipeline network; the equipment operation status association interface refers to the interface set at the treatment facility model to associate the actual operation status data of the treatment facility; accurately locate the coordinates of each emission point, the pipeline network direction and depth, and the location of the treatment facility in the initial 3D model; add the corresponding sensor number association identifier at the coordinates of each emission point to ensure that each emission point is accurately bound to the sensor number monitoring that point; annotate the hydraulic transmission path along the pipeline network direction to clearly present the flow direction and transmission path of sewage within the pipeline network; integrate the equipment operation status association interface at the treatment facility location, and after completing all the above enhanced annotation operations, a complete 3D twin mirror is obtained.

[0040] Step S1024: Based on the sensor number association identifier, hydraulic transmission path label, and equipment operation status association interface, establish the association relationship between the structured dataset and the three-dimensional twin mirror.

[0041] Furthermore, the association relationship refers to the rules used to establish a mapping between various monitoring data in the structured dataset and corresponding elements in the 3D twin mirror. During implementation, the sensor numbers and monitoring objects (emission points, pipe networks, treatment facilities, etc.) corresponding to various data types in the structured dataset are first parsed. Using sensor number association identifiers as a bridge, the association between the sensor-collected data in the structured dataset and the corresponding emission points in the mirror is established. Based on hydraulic transmission path annotations, the association between pipe network-related monitoring data (such as pipe network water level and flow velocity data) and the corresponding pipe network segments in the mirror is established. Through the equipment operation status association interface, the association between treatment facility-related monitoring data (such as treatment facility inlet and outlet flow rates and pH value data) and the corresponding treatment facilities in the mirror is established. These association rules are then integrated to form a complete association relationship between the structured dataset and the 3D twin mirror.

[0042] Step S1025: Using a data mapping engine, the structured dataset is mapped to the corresponding points of the 3D twin mirror based on the association relationship.

[0043] Furthermore, the data mapping engine refers to the core module with data parsing, association matching, and real-time mapping functions, used to accurately push structured data to the corresponding positions in the 3D twin mirror according to the association relationships. Upon starting the data mapping engine, the established association relationships are imported into the engine's internal association rule base; the data mapping engine performs real-time parsing of various types of data in the structured dataset, extracting the monitoring object information corresponding to the data; based on the parsing results, it matches the corresponding association relationships in the association rule base to determine the target mapping point of the data in the 3D twin mirror; the parsed structured data is pushed to the target mapping point in real time, achieving accurate mapping between the data and the mirror point, ensuring that the status of each element in the mirror can be updated in real time through the mapped data.

[0044] Step S1026: Based on the core elements in the three-dimensional twin mirror, a spatial correlation analysis algorithm is generated to construct a spatial topological correlation. The spatial topological correlation represents the correlation between the emission source, pipeline segment, pipeline node, treatment facility and surrounding water body.

[0045] Furthermore, the core elements refer to the key control elements contained in the 3D twin mirror, such as emission sources, pipeline segments, pipeline nodes, treatment facilities, and surrounding water bodies; the spatial correlation analysis algorithm refers to the algorithm used to analyze the spatial positional relationships, connectivity relationships, and influence paths of each core element in the mirror; pipeline nodes refer to the key nodes in the pipeline system used to connect different pipeline segments, such as pipeline junctions and corners; the core elements in the 3D twin mirror are extracted to obtain information such as the spatial coordinates, dimensions, and connectivity relationships of each element; based on this core element information, a spatial correlation analysis algorithm is designed and generated, and the algorithm needs to have the function of analyzing the spatial distance, connectivity, and influence range between elements; the core element information is input into the spatial correlation analysis algorithm, and the algorithm analyzes the connection relationship between emission sources and pipeline segments, the connection relationship between pipeline segments and pipeline nodes, the connectivity path between pipeline nodes and treatment facilities, and the spatial positional relationship between treatment facilities and surrounding water bodies through algorithm calculation, and constructs spatial topological correlation to ensure that the correlation can completely represent the various correlation relationships between emission sources, pipeline segments, pipeline nodes, treatment facilities, and surrounding water bodies.

[0046] Step S103: Using time-series segmented analysis algorithm and emission prediction model, obtain dynamic threshold for anomaly determination based on structured dataset and historical data from the same period.

[0047] Specifically, step S103 includes: Step S1031: Obtain historical data for the same period. The historical data for the same period is the time series data of rural sewage discharge in the same season and at the same time over the past 3 years.

[0048] Furthermore, historical contemporaneous data refers to time-series data of rural sewage discharge from multiple consecutive complete years that fall within the same season and time period as the current data collection phase. This data maintains consistency with the parameter types of the current structured dataset, including various monitoring parameters such as sewage discharge flow rate and sewage COD concentration. The system accesses a preset historical data repository and sets data filtering criteria based on the seasonal and time period information of the current data collection. Based on these criteria, the system extracts the corresponding rural sewage discharge time-series data from the historical data repository. The extracted historical contemporaneous data undergoes integrity verification, eliminating severely missing or invalid data segments. The verified historical contemporaneous data is then categorized and organized to ensure that its parameter types and data formats match the structured dataset, providing a qualified data foundation for subsequent analysis and processing.

[0049] Step S1032: The structured dataset and historical data from the same period are segmented using a time-series segmentation analysis algorithm to identify peak characteristics of wastewater discharge and generate time-specific normal thresholds.

[0050] Furthermore, the time-series segmentation analysis algorithm refers to an algorithm that divides continuous time-series data based on the time dimension, making the data characteristics within each segment relatively consistent. Segmentation processing refers to using the algorithm to divide the integrated structured dataset and historical data from the same period into multiple continuous data segments according to time patterns. Wastewater discharge peak characteristics refer to the characteristic of wastewater discharge volume or pollutant concentration being significantly higher than other periods. The time-specific normal threshold refers to the benchmark judgment value determined based on normal discharge data within different time periods. The structured dataset and the processed historical data from the same period are aligned to ensure collaborative analysis in the time dimension. The aligned integrated data is input into the time-series segmentation analysis algorithm, which automatically identifies data feature mutation points and divides the integrated data into multiple continuous time periods based on these mutation points. Feature analysis is performed on the data within each time period to identify wastewater discharge peak characteristics. Based on the data distribution patterns under normal discharge conditions within each time period, the time-specific normal threshold corresponding to each time period is determined, forming a set of time-specific normal thresholds covering the entire monitoring period.

[0051] Step S1033: Input the structured dataset and historical data from the same period into the emission prediction model to obtain the predicted wastewater discharge data for the future preset period.

[0052] Furthermore, the emission prediction model refers to a model trained on historical data that has the ability to predict future wastewater discharge status based on existing data; the future preset time period refers to a pre-defined future time period for which wastewater discharge status prediction is required; wastewater discharge prediction data refers to data output by the emission prediction model that reflects the changing trends of various parameters of wastewater discharge within the future preset time period. The structured dataset and historical data from the same period are preprocessed to remove outliers and ensure the validity of the input data. The preprocessed structured dataset and historical data from the same period are then input into the emission prediction model according to a preset format, with the structured dataset serving as real-time input data and the historical data from the same period serving as model reference data. The model's prediction output parameters are set, specifying the types of wastewater discharge parameters to be predicted and the time range of the future preset time period. The emission prediction model is then started for computation. Through correlation analysis between real-time and historical data, the model outputs wastewater discharge prediction data for the future preset time period. The predicted data must maintain consistency with the parameter types of the structured dataset.

[0053] Step S1034: Calculate the residual vector between the predicted wastewater discharge data and the real-time monitoring data, and iteratively optimize the time-period normal threshold through residual feedback to obtain the dynamic threshold for anomaly determination.

[0054] Furthermore, the residual vector refers to the vector constructed according to a preset dimension, representing the difference between the predicted wastewater discharge data and the real-time monitoring data; the real-time monitoring data refers to the wastewater discharge-related data collected in real-time by the sensor network and preliminarily processed; residual feedback iterative optimization refers to the process of repeatedly adjusting the time-specific normal threshold using the residual vector as feedback information until the preset optimization conditions are met; the anomaly judgment dynamic threshold refers to the anomaly judgment benchmark value that can be dynamically adjusted according to the wastewater discharge status after iterative optimization. The process involves acquiring real-time monitoring data corresponding to the time period of the predicted wastewater discharge data, ensuring accurate matching between the two in terms of time dimension and parameter type; calculating the difference between the predicted wastewater discharge data and the corresponding real-time monitoring data, constructing a residual vector according to the preset parameter dimensions; inputting the residual vector as feedback information into the threshold optimization module, adjusting the time-specific normal threshold based on the magnitude and direction of the residual vector; repeating the above residual calculation and threshold adjustment process, performing multiple rounds of residual feedback iterative optimization until the residual vector meets the preset convergence condition, at which point the obtained threshold is the anomaly judgment dynamic threshold.

[0055] Step S104: Determine the anomaly level and quantify the impact range data based on the structured dataset, spatial topological association, and dynamic threshold for anomaly detection.

[0056] Specifically, step S104 includes: Step S1041: Determine the anomaly level based on real-time monitoring data, dynamic threshold for anomaly determination, and preset standards in the structured dataset.

[0057] Furthermore, the preset standard refers to a pre-established rule system used to classify anomaly levels based on the degree of deviation between real-time monitoring data and dynamic thresholds for anomaly determination; the anomaly level refers to the hierarchical classification of the severity of sewage discharge anomalies, used to distinguish the priority of handling different anomalies. The latest real-time monitoring data is selected from the structured dataset, and the data is validated to remove invalid data caused by transmission delays or temporary sensor malfunctions. The validated real-time monitoring data is compared one by one with the corresponding dynamic thresholds for anomaly determination to obtain the deviation of each monitoring parameter from the threshold. Based on the preset standard, the deviation is matched to determine the anomaly level corresponding to the current sewage discharge, and the deviation details of each monitoring parameter are recorded as supplementary explanations for the anomaly level determination results.

[0058] Step S1042: Use spatial correlation analysis algorithm to generate quantitative influence range data based on structured data and spatial topological correlation.

[0059] Furthermore, quantitative impact range data refers to a dataset containing quantitative indicators such as the spatial boundaries, coverage areas, and degree of impact of anomalies. This involves organizing the anomaly-related monitoring data from the structured data, including the monitoring parameters and monitoring points corresponding to the anomalies; importing the organized structured data and the established spatial topology associations into a spatial correlation analysis algorithm; analyzing the spatial location of the anomaly monitoring points to trace their connectivity with surrounding pipe network segments, pipe network nodes, treatment facilities, and sensitive water bodies; and based on connectivity and anomaly monitoring data, calculating the potential spatial range of the abnormal discharge, determining the impact boundaries, and simultaneously quantitatively analyzing the degree of impact on various related elements. This information is then integrated to form standardized quantitative impact range data.

[0060] Step S105: Using a reverse tracing algorithm, the abnormal emission source is located and abnormal emission information is obtained based on spatial topological association, anomaly level, and quantified impact range data.

[0061] Specifically, step S105 includes: Step S1051: Based on the spatial topological association model, anomaly level determination results, and quantified impact range data, construct an anomaly source tracing data matrix containing spatial node weights and anomaly impact coefficients.

[0062] Furthermore, spatial node weight refers to the weight value characterizing the importance of each spatial node in the wastewater transmission network; the anomaly impact coefficient refers to the coefficient characterizing the degree of impact of abnormal emissions on each spatial node; the anomaly source tracing data matrix refers to a quantitative data matrix constructed with spatial nodes as rows and spatial node weights and anomaly impact coefficients as columns, used to provide decision-making basis for reverse tracing. Information on all spatial nodes in the spatial topology association model is extracted to clarify the type and connection relationship of each node; the allocation rules for the anomaly impact weight coefficient are determined based on the anomaly level judgment results, and the degree of impact of anomalies on each spatial node is determined based on the quantitative impact range data; each spatial node is assigned a corresponding spatial node weight and anomaly impact coefficient, and these data are integrated into an anomaly source tracing data matrix in a format where rows and columns represent spatial nodes and columns represent association attributes, ensuring that each element in the matrix accurately corresponds to the quantitative association characteristics of a single spatial node.

[0063] Step S1052: Import the abnormal source tracing data matrix into the reverse tracing algorithm. Using the boundary node of the quantified impact range as the starting point for tracing, traverse the hydraulic transmission path of the pipeline network in reverse to select candidate abnormal emission source locations that match the abnormality level.

[0064] Furthermore, boundary nodes refer to the network nodes at the edge of the quantified impact range; the network hydraulic transmission path refers to the path of sewage flow within the network; and candidate abnormal emission source locations refer to the locations that may generate abnormal emissions, initially screened through reverse tracing. The constructed abnormal source tracing data matrix is ​​imported into the reverse tracing algorithm to complete the data adaptation between the algorithm and the matrix; boundary node information is extracted from the quantified impact range data, and these boundary nodes are set as the source tracing starting points; based on the spatial node weights in the abnormal source tracing data matrix, the algorithm selects priority tracing paths with higher weights, and traverses the network hydraulic transmission path in reverse along these priority tracing paths; during the tracing process, the matching degree between the nodes corresponding to each path and the abnormality level is determined by combining the abnormality impact coefficient in the matrix, and the emission locations corresponding to the nodes whose matching degree meets the preset requirements are retained, forming a set of candidate abnormal emission source locations.

[0065] Step S1053: Based on the abnormal diffusion area, pipeline hydraulic transmission velocity and fluid dynamic characteristics in the quantitative impact range data, calculate the abnormal diffusion time and determine the abnormal emission time window of the candidate abnormal emission source location.

[0066] Furthermore, the abnormal diffusion area refers to the spatial area covered by the abnormally emitted pollutants; the hydraulic transmission velocity of the pipe network refers to the flow velocity of sewage within the pipe network; the fluid dynamics characteristics refer to the physical properties of sewage such as viscosity and inertia when flowing within the pipe network; the abnormal diffusion time refers to the time required for abnormal pollutants to diffuse from the emission source to the boundary of the quantified impact range; and the abnormal emission time window refers to the time period during which the abnormal emission occurs. Abnormal diffusion area information is extracted from the quantified impact range data, and measured data of the hydraulic transmission velocity of the pipe network are obtained to clarify the fluid dynamics characteristic parameters of the sewage. An abnormal diffusion time calculation model is constructed based on fluid dynamics principles. The abnormal diffusion area, the hydraulic transmission velocity of the pipe network, and the fluid dynamics characteristic parameters are input into the model to calculate the abnormal diffusion time. Based on the time when the abnormality is detected, the start and end times of the abnormal emission are derived in reverse, determining the abnormal emission time window corresponding to each candidate abnormal emission source location.

[0067] Step S1054: Based on the abnormal emission time window, the candidate abnormal emission source locations are cross-validated using the acquisition time difference to locate the abnormal emission source.

[0068] Furthermore, the acquisition time difference refers to the time difference in the acquisition of anomaly-related data by sensors at different locations; cross-validation refers to judging the rationality of candidate locations by cross-verifying multiple sets of data. The acquisition data of the corresponding sensors at each candidate abnormal emission source location within its abnormal emission time window are extracted, and the acquisition data of the downstream pipeline node sensors at these candidate locations during the same period are also extracted. The time difference in the acquisition of abnormal characteristic data by sensors at different locations is analyzed, and the time for the abnormal characteristic data to propagate from the candidate location to the downstream node is verified in conjunction with the hydraulic transmission velocity of the pipeline network to see if it matches the acquisition time difference. The above cross-validation is performed on each candidate abnormal emission source location, and candidate locations that fail the verification are eliminated. The location that passes the verification and has the highest data matching degree is retained and identified as the abnormal emission source.

[0069] Step S1055: Obtain the precise spatial coordinates, anomaly level, abnormal emission time window, abnormal indicator exceedance type and exceedance range of the abnormal emission source, and integrate them to obtain abnormal emission information.

[0070] Furthermore, precise spatial coordinates refer to the accurate location coordinates of the abnormal emission source in physical space; abnormal indicator exceedance type refers to the type of monitoring parameter that exceeds the dynamic threshold for anomaly determination; exceedance magnitude refers to the degree to which the monitoring parameter exceeds the dynamic threshold for anomaly determination; and abnormal emission information refers to a standardized data set integrating various core characteristics of the abnormal emission source. The precise spatial coordinates of the abnormal emission source are extracted from the 3D twin image; the anomaly level determination result and the abnormal emission time window are retrieved; the monitoring data of the corresponding sensor for the abnormal emission source within the abnormal emission time window are extracted from the structured dataset to determine the abnormal indicator exceedance type and calculate and determine the exceedance magnitude; the precise spatial coordinates, anomaly level, abnormal emission time window, abnormal indicator exceedance type, and exceedance magnitude are integrated according to a preset format to form complete abnormal emission information.

[0071] Step S106: Generate a structured assessment report based on abnormal emission information, and generate hierarchical collaborative control instructions based on the assessment report.

[0072] Specifically, step S106 includes: Step S1061: Extract the core data from the abnormal emission information and generate a structured assessment report containing an anomaly overview, source tracing results, impact range, and disposal recommendations in a preset format.

[0073] Furthermore, core data refers to key data characterizing the core features of the anomaly in the abnormal emission information, including precise spatial coordinates and anomaly level; preset format refers to the pre-defined report structure, data presentation method, and expression standards; anomaly overview refers to a summary description of the basic situation of the abnormal emission; source tracing results refer to conclusive information related to the location of the abnormal emission source; impact range refers to relevant data on the impact of the abnormal emission on surrounding elements; disposal recommendations refer to targeted treatment plans proposed for the abnormal emission; and a structured assessment report refers to a standardized analysis report with a fixed logical structure and complete data specifications. Core data is extracted from the abnormal emission information, redundant information is eliminated, and the extracted data accurately covers the core features of the anomaly; the report is divided into chapters according to the preset format, and the anomaly overview, source tracing results, impact range, and disposal recommendations are filled in sequentially. The anomaly overview clearly identifies the core parameters of the anomaly, the source tracing results clearly present the location information of the abnormal emission source, the impact range details the spatial range and degree of impact of the anomaly's spread, and the disposal recommendations propose appropriate treatment measures based on the anomaly level; the report content undergoes logical verification and format standardization to ensure that the information in each chapter is coherent and the data is accurate, ultimately generating a structured assessment report.

[0074] Step S1062: Based on the anomaly level and handling recommendations, match the target classification control standards to determine the control priority and the scope of linked equipment.

[0075] Furthermore, the hierarchical control standard refers to a pre-established standardized system that classifies control requirements and clarifies control measures according to the level of anomaly; the target hierarchical control standard refers to the specific control standard that matches the current anomaly level and handling recommendations; the control priority refers to the order of handling determined according to the severity of the anomaly; and the scope of coordinated equipment refers to the set of various equipment that need to coordinate actions to implement control measures. The process involves retrieving the anomaly level and handling recommendations from the structured assessment report to clarify the severity of the current anomaly and the core handling requirements; accessing the pre-set hierarchical control standard library to initially screen the appropriate control standard range based on the anomaly level; combining the specific measures required in the handling recommendations to accurately match the target hierarchical control standard from the initially screened range; determining the control priority based on the target hierarchical control standard to clarify the handling order of the current anomaly relative to other potential anomalies; and simultaneously analyzing the equipment action requirements specified in the target hierarchical control standard to compile a list of equipment requiring coordinated action and clarify the scope of coordinated equipment.

[0076] Step S1063: Generate hierarchical collaborative control instructions based on the target hierarchical control standard. The control instructions include equipment action instructions, monitoring frequency adjustment instructions, and information push instructions.

[0077] Furthermore, equipment action commands refer to commands used to control linked equipment to perform specific actions; monitoring frequency adjustment commands refer to commands used to adjust the reporting frequency of sensor monitoring data; information push commands refer to commands used to push abnormal information to designated control terminals; and hierarchical collaborative control commands refer to a comprehensive set of commands that integrates various specialized commands to achieve multi-device collaboration and multi-stage control. Based on the specific requirements for equipment actions in the target hierarchical control standards, corresponding equipment action commands are generated for different devices within the scope of the linked equipment, clarifying key parameters such as the action type and execution sequence of each device; monitoring frequency adjustment commands are generated according to the abnormality level and handling progress requirements to adapt and adjust the sensor monitoring frequency around and within the impact range of the abnormal emission source; information push commands are generated based on control priorities and information transmission needs, clarifying the pushed information content, target audience, and push method; and the equipment action commands, monitoring frequency adjustment commands, and information push commands are integrated according to a preset format, labeling the corresponding control level and execution requirements of the commands, ultimately forming a complete hierarchical collaborative control command system.

[0078] In some alternative embodiments, the method further includes: Step a1: Issue hierarchical collaborative control instructions to the corresponding linked equipment and control terminals, collect equipment execution status data and on-site monitoring data in real time, and obtain control feedback data.

[0079] Furthermore, "linked equipment" refers to various devices that require coordinated action, including inlet valves and monitoring sensors; "control terminal" refers to terminal devices used to receive control commands, display abnormal information, and provide feedback on handling status; "equipment execution status data" refers to the status information of the actual actions performed by the linked equipment after receiving the command, including whether the action was completed and the execution accuracy; "on-site monitoring data" refers to the wastewater discharge-related data collected in real time by sensors around and within the affected area of ​​the abnormal discharge source; and "control feedback data" refers to a comprehensive data set that integrates equipment execution status data and on-site monitoring data to reflect the effectiveness of control measures. Hierarchical coordinated control commands are sent to the corresponding linked equipment and control terminal through a pre-set communication link to ensure complete and accurate command transmission; a real-time data acquisition process is initiated to continuously acquire execution status data from each linked equipment, while simultaneously collecting on-site monitoring data reported by sensors around and within the affected area of ​​the abnormal discharge source at an adjusted frequency; the two types of data are initially processed, invalid data caused by transmission interference is removed, and valid data is aligned and integrated according to timestamps to form control feedback data.

[0080] Step a2: Verify whether the anomaly has been eliminated based on the control feedback data and the dynamic threshold for anomaly determination.

[0081] Furthermore, the latest on-site monitoring data is extracted from the control feedback data, including various core monitoring parameters such as wastewater discharge flow rate, COD concentration, and pH value. The extracted on-site monitoring data is compared one by one with the corresponding dynamic thresholds for anomaly detection to analyze whether each monitoring parameter is within the normal range defined by the dynamic thresholds for anomaly detection. At the same time, combined with the equipment execution status data in the control feedback data, it is confirmed whether the linkage equipment has completed the preset actions as instructed, so as to avoid the distortion of monitoring data due to the equipment not performing the actions normally. The results of the monitoring parameter comparison and the equipment execution status are combined to verify whether the anomaly has been eliminated.

[0082] Step a3: If the anomaly has been eliminated, record the control process and update it to the 3D twin image to form a control file.

[0083] Furthermore, the control process refers to the entire process information from anomaly identification, source tracing, assessment, instruction generation to anomaly elimination, including time nodes, implementation measures, and core data for each stage; the control file refers to a standardized file formed by integrating the entire control process information for subsequent traceability and management; the update of the 3D twin mirror refers to associating the control process information with the corresponding points in the mirror, realizing the synchronous recording of the physical control process and the virtual mirror. The entire process information from anomaly identification to anomaly elimination is compiled, including anomaly identification time, anomaly level, anomaly emission source information, generated assessment report content, details of hierarchical collaborative control instructions, equipment execution status, and monitoring data change trends; the compiled control process information is organized and archived according to a preset file format to form a complete control file; the control file is associated with the corresponding anomaly emission source points and affected areas in the 3D twin mirror, and the information association content of the mirror is updated, so that the 3D twin mirror completely records the entire process of this anomaly control, realizing the visual traceability of the control process.

[0084] Step a4: If the anomaly is not eliminated, upgrade the control level based on the control feedback data and generate a feedback hierarchical collaborative control instruction until the anomaly is eliminated.

[0085] Furthermore, control level upgrade refers to raising the original control level to a higher level based on the reasons for the persistent anomaly and control feedback data, corresponding to stricter control measures. Feedback-based hierarchical collaborative control instructions refer to more targeted collaborative control instructions generated based on the upgraded control level and control feedback data. The process involves analyzing the reasons for the persistent anomaly, judging the degree of persistence and spread of the anomaly by combining on-site monitoring data from the control feedback data, and confirming whether there are any issues with equipment execution. Based on the cause analysis results, the original control level is upgraded to the corresponding higher level according to the preset hierarchical control standards. Based on the upgraded control level and the actual on-site situation reflected in the control feedback data, control measures are adjusted, and feedback-based hierarchical collaborative control instructions are generated. These instructions need to further strengthen equipment action requirements, increase monitoring frequency, or expand the scope of linked equipment. The feedback-based hierarchical collaborative control instructions are then issued to the corresponding linked equipment and control terminals. Steps a1 to a2 are repeated, continuously collecting control feedback data and verifying whether the anomaly has been eliminated until it is completely eliminated.

[0086] The digital twin-based rural construction digital management method provided in this embodiment firstly involves classifying and deploying a sensor network based on a deployment strategy. This collects time-series data from various regions and processes it to obtain a structured dataset, achieving differentiated adaptation and standardized preprocessing of rural sewage control data collection. Sensors of appropriate types are matched to the emission characteristics of different rural areas, ensuring the accuracy and stability of data collection. Systematic processing techniques such as outlier removal, unit standardization, and time stamp standardization effectively improve data quality and eliminate noise and format differences in the original data. Secondly, by constructing a three-dimensional twin mirror, the structured dataset is mapped to corresponding points in the mirror, establishing spatial topological relationships. This achieves precise mapping and real-time linkage between the physical control scenario and the virtual digital model. The three-dimensional twin mirror construction technology can accurately replicate the spatial attributes and physical characteristics of core elements such as emission sources, pipe networks, and treatment facilities. The data mapping technology achieves a one-to-one correspondence between structured data and mirror points, while the spatial topological relationship construction technology forms a computable and traceable spatial relationship model by sorting out the connectivity and influence paths between various elements. This provides a visual and interactive technical carrier for subsequent spatial-dimensional algorithm analysis and control decisions. Then, by employing a time-series segmentation analysis algorithm and an emission prediction model, combined with structured datasets and historical data from the same period, a dynamic threshold for anomaly detection is obtained. This technically overcomes the limitations of traditional fixed threshold detection, achieving intelligent and dynamic anomaly detection. The time-series segmentation analysis algorithm can accurately capture the temporal and periodic characteristics of wastewater discharge, providing precise temporal basis for threshold division. The emission prediction model learns the correlation patterns between historical data from the same period and real-time structured data to predict wastewater discharge trends. The two work together to generate a dynamic threshold through residual feedback iteration, enabling the anomaly detection standard to adapt to the dynamic changes in rural wastewater discharge in real time. This improves the accuracy of anomaly identification at the algorithmic level and effectively reduces the technical risks of misjudgment and missed judgment. Furthermore, by combining structured datasets, spatial topological correlations, and dynamic thresholds for anomaly detection to determine anomaly levels and quantify the scope of impact, a multi-dimensional and accurate quantitative assessment of anomalies is achieved. By integrating the dual technological advantages of data-driven and spatial modeling, the system achieves scientific classification of anomaly levels through multi-dimensional data fusion analysis. It accurately defines the spatial boundaries and extent of anomaly impacts using spatial topological correlation models and quantitative analysis techniques. The generated quantitative data has clear technical indicator significance, providing precise technical basis for subsequent management resource allocation and management strategy formulation, and improving the pertinence and scientific nature of anomaly handling.Furthermore, by employing a reverse tracing algorithm, abnormal emission sources are located and abnormal emission information is obtained based on spatial topological correlation, anomaly level, and quantified impact range data. Utilizing path guidance provided by the spatial topological correlation model, a source tracing weight matrix is ​​constructed by combining anomaly level and impact range data, rapidly narrowing the tracing scope and improving efficiency. Simultaneously, multi-source data cross-validation technology further ensures tracing accuracy. The final integrated abnormal emission information includes precise spatial coordinates, exceedance types, and other multi-dimensional technical parameters, providing precise technical targets for anomaly response. Finally, by generating a structured assessment report based on the abnormal emission information, and combining this report with hierarchical collaborative control instructions, a closed-loop intelligent management system for rural sewage anomalies is achieved. The structured assessment report generation technology systematically integrates technical data from multiple stages, including anomaly assessment and source tracing, forming standardized technical documents. The hierarchical collaborative control instruction generation technology achieves precise matching of control levels and measures based on the assessment results. Through equipment linkage control and dynamic adjustment of monitoring frequency, it enables precise allocation of control resources and multi-stage collaborative linkage, constructing a technical closed loop. This effectively improves the technical efficiency and control effect of anomaly response, promoting the transformation of rural sewage management from traditional experience-based to modern technology-driven approaches. By implementing this invention, we can solve the problems that current decentralized rural domestic sewage management technologies generally suffer from, such as insufficient adaptability, low monitoring accuracy, weak data processing and correlation capabilities, low efficiency in anomaly tracing and control, and lack of closed-loop management mechanisms. These shortcomings prevent us from meeting the actual needs of precise, efficient, and low-cost rural sewage management.

[0087] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A digital management method for rural construction based on digital twins, characterized in that, The method includes: The time-series data returned by the sensor network is collected, and the time-series data is processed to obtain a structured dataset. The sensor network is then deployed according to a deployment strategy. Construct a three-dimensional twin mirror image, map the structured dataset to the corresponding points in the three-dimensional twin mirror image, and establish spatial topological associations; The dynamic threshold for anomaly detection is obtained based on the structured dataset and historical data from the same period using a time-series segmented analysis algorithm and an emission prediction model. Anomaly levels and impact ranges are determined based on structured datasets, spatial topological associations, and dynamic thresholds for anomaly detection. Using a reverse tracing algorithm, the abnormal emission source is located and abnormal emission information is obtained based on the spatial topology association, the anomaly level, and the quantified impact range data; A structured assessment report is generated based on the abnormal emission information, and a hierarchical collaborative control instruction is generated based on the assessment report.

2. The method according to claim 1, characterized in that, The deployment strategy is based on deployment zones, which include concentrated farmer areas, independent farmer areas, public facility areas, key pipeline node areas, and treatment facility areas. The deployment strategy includes: deploying ultrasonic flow meters and COD / pH dual-parameter sensors at the septic tank outlets in the concentrated farmer areas; deploying passive flow monitoring sensors at the courtyard drainage outlets in the independent farmer areas; deploying ammonia nitrogen / COD dual-parameter monitoring sensors and anomaly marker integrated monitoring sensors at the outlets of the public facility areas; deploying integrated water level and flow velocity sensors in the key pipeline node areas; and deploying flow and pH sensors interlocked with the inlet valves at the inlet and outlet of the treatment facility areas.

3. The method according to claim 2, characterized in that, The time-series data returned by the sensor network is collected, and the time-series data is processed to obtain a structured dataset, including: Collect time-series data within each deployment partition. The time-series data includes wastewater discharge flow rate data, wastewater COD concentration data, wastewater pH value data, wastewater ammonia nitrogen concentration data, pipeline water level data, pipeline flow velocity data, inlet and outlet flow rate data of treatment facilities, and inlet and outlet pH value data of treatment facilities. The time-series data is transmitted to the multi-source data fusion gateway using a dual-mode approach of timed reporting and exception triggering. The time-series data is processed sequentially using a multi-source data fusion gateway to remove outliers, fill in missing values, unify data units, and standardize timestamps, thereby generating a structured dataset.

4. The method according to claim 3, characterized in that, The construction process of the three-dimensional twin mirror includes: Extract prior feature information on the distribution of emission sources, pipeline routes, coordinates of treatment facilities, and the scope of sensitive water bodies within each deployment zone, and establish a feature information list; Acquire low-altitude photographic image data and spatial location data from UAVs, and use point cloud fusion algorithms to generate an initial 3D model based on the UAV low-altitude photographic image data and spatial location data. On the initial three-dimensional model, sensor number association identifiers are added to the coordinates of the emission points, hydraulic transmission path labels are added to the pipeline route and burial depth, and equipment operation status association interfaces are added to the locations of the treatment facilities to obtain a three-dimensional twin mirror.

5. The method according to claim 4, characterized in that, The step of mapping the structured dataset to corresponding points in the 3D twin mirror image and establishing spatial topological associations includes: Based on the sensor number association identifier, the hydraulic transmission path label, and the equipment operation status association interface, establish the association relationship between the structured dataset and the three-dimensional twin mirror; The structured dataset is mapped to the corresponding points of the 3D twin image based on the aforementioned relationship using a data mapping engine. Based on the core elements generated in the three-dimensional twin mirror, a spatial correlation analysis algorithm is used to construct a spatial topological correlation, which represents the correlation between emission sources, pipeline segments, pipeline nodes, treatment facilities and surrounding water bodies.

6. The method according to claim 5, characterized in that, The process of obtaining dynamic thresholds for anomaly detection based on the structured dataset and historical data from the same period using time-series segmented analysis algorithms and emission prediction models includes: Obtain historical data for the same period, which refers to the time series data of rural sewage discharge in the same season and at the same time over the past 3 years; The structured dataset and historical data from the same period were segmented using a time-series segmentation analysis algorithm to identify peak characteristics of wastewater discharge and generate time-specific normal thresholds. The structured dataset and historical data from the same period are input into the emission prediction model to obtain wastewater emission prediction data for a future preset period. The residual vector between the predicted wastewater discharge data and the real-time monitoring data is calculated, and the time-specific normal threshold is iteratively optimized through residual feedback to obtain the dynamic threshold for anomaly determination.

7. The method according to claim 6, characterized in that, The data used to determine anomaly levels and quantify the scope of impact based on structured datasets, spatial topological associations, and dynamic thresholds for anomaly detection includes: The anomaly level is determined based on the real-time monitoring data in the structured dataset, the dynamic threshold for anomaly detection, and the preset standard. Based on the structured data and the spatial topological correlation, a spatial correlation analysis algorithm is used to generate quantitative influence range data.

8. The method according to claim 7, characterized in that, The method of using a reverse tracing algorithm to locate abnormal emission sources and obtain abnormal emission information based on the spatial topological association, the anomaly level, and the quantified impact range data includes: Based on the spatial topology association model, the anomaly level determination results, and the quantified impact range data, an anomaly source tracing data matrix containing spatial node weights and anomaly impact coefficients is constructed. The abnormal source tracing data matrix is ​​imported into the reverse tracing algorithm. Taking the boundary node of the quantified impact range as the source tracing starting point, the hydraulic transmission path of the pipeline network is traversed in reverse to filter out candidate abnormal emission source locations that match the abnormality level. Based on the abnormal diffusion area, pipeline hydraulic transmission velocity, and fluid dynamic characteristics in the quantified impact range data, the abnormal diffusion time is calculated to determine the abnormal emission time window of the candidate abnormal emission source location. Based on the abnormal emission time window, the candidate abnormal emission source locations are cross-validated using the time difference of data collection to locate the abnormal emission source. The precise spatial coordinates, anomaly level, abnormal emission time window, type and magnitude of exceeding the abnormal indicators of the abnormal emission source are obtained and integrated to obtain abnormal emission information.

9. The method according to claim 8, characterized in that, The process of generating a structured assessment report based on the abnormal emission information, and generating tiered collaborative control instructions based on the assessment report, includes: Extract the core data from the abnormal emission information and generate a structured assessment report in a preset format, which includes an anomaly overview, source tracing results, scope of impact, and disposal recommendations. Based on the anomaly level and the handling recommendations, the target hierarchical control standards are matched to determine the control priority and the scope of linked equipment; Based on the target-based hierarchical control standard, hierarchical collaborative control instructions are generated, which include equipment action instructions, monitoring frequency adjustment instructions, and information push instructions.

10. The method according to claim 9, characterized in that, The method further includes: The hierarchical collaborative control instructions are sent to the corresponding linked equipment and control terminals, and the equipment execution status data and on-site monitoring data are collected in real time to obtain control feedback data; Verify whether the anomaly has been eliminated based on the control feedback data and the anomaly determination dynamic threshold. If the anomaly has been eliminated, record the control process and update it to the three-dimensional twin image to form a control file; If the anomaly is not eliminated, the control level will be upgraded based on the control feedback data, and a feedback-level collaborative control instruction will be generated until the anomaly is eliminated.

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