Intelligent water affair whole-process monitoring method and system

Abstract

This invention provides a smart water management full-process monitoring method and system, involving data processing technology. The method includes: acquiring the required wastewater discharge volume corresponding to each production equipment located in a production plant; acquiring the actual wastewater discharge volume corresponding to each wastewater discharge pipeline connected to different production equipment in the production plant based on a discharge monitoring unit; determining that the required discharge volume corresponding to the same pipeline connection is greater than the actual discharge volume, identifying the production equipment as abnormal equipment, and grouping adjacent abnormal equipment with corresponding distances less than a preset interval into the same density group; detecting each hidden pipe monitoring plot located at the edge of the production plant based on the density group, and determining that any hidden pipe monitoring plot contains a wastewater discharge pipe based on the detection results, and marking it based on a top view of the corresponding production plant. This invention at least improves the accuracy of corresponding hidden pipe detection.

Description

Technical Field

[0001] This invention relates to data processing technology, and more particularly to a smart water management full-process monitoring method and system. Background Technology

[0002] With the continuous improvement of my country's water environment protection and governance system and the continuous enhancement of smart water digital supervision capabilities, the compliant discharge management of industrial production plants has become a core link in the prevention and control of water pollution in river basins. Among them, production plants, as key entities for industrial wastewater discharge, directly affect the quality of the surrounding water ecological environment due to the compliance of their wastewater discharge behavior. The most common and difficult-to-monitor illegal discharge behavior in industrial wastewater is currently the most concealed behavior among those that involve burying hidden wastewater discharge pipes underground and bypassing the compliance discharge monitoring system.

[0003] For the investigation and location of such hidden pipes, manual investigation is currently the most widely used basic implementation method in grassroots environmental law enforcement and corporate compliance self-inspection. The accuracy of the investigation results directly determines the effectiveness of supervision and disposal of illegal sewage discharge, and has become a key issue that urgently needs to be solved in the construction of a smart water affairs full-process closed-loop supervision system.

[0004] The current manual search methods for hidden pipes in wastewater discharge from production plants are mainly divided into experience-based manual inspections. That is, the inspectors usually first roughly estimate the total theoretical amount of wastewater generated by the entire plant through the overall production plan and raw material procurement ledgers, and then combine the flow monitoring data of the plant's main discharge outlet to determine whether there is a wastewater discharge gap. Based on their own professional experience, they conduct manual inspections in the plant area and surrounding areas, and finally verify the existence of the hidden pipe by manually excavating some areas.

[0005] It is evident that the aforementioned method of concealed pipe inspection, which relies primarily on manual searching, suffers from inherent defects in its inaccuracy during practical application. It can only define a vague inspection range based on experience, making it prone to serious deviations in the inspection direction. This reduces the accuracy of concealed pipe detection from the source, resulting in extremely low inspection efficiency and a high risk of large-scale blind spots due to omissions in manual scanning paths and non-standard operation, leading to frequent cases of missed or false detections of concealed pipes.

[0006] Therefore, there is an urgent need to provide a smart water management full-process monitoring method and system that can improve the accuracy of corresponding concealed pipe detection. Summary of the Invention

[0007] Based on the above problems, the present invention is proposed to provide a smart water affairs full-process monitoring method and system that overcomes or at least partially solves the above problems.

[0008] According to one aspect of the present invention, a method for monitoring the entire process of smart water management is provided, comprising: Obtain the required wastewater discharge amount for each production equipment located in the production plant, and obtain the actual wastewater discharge amount for each wastewater discharge pipeline located in the production plant that has pipeline connection with different production equipment based on the discharge monitoring unit; If the response determines that the required emission amount corresponding to the same pipeline connection is greater than the actual emission amount, the production equipment is identified as abnormal equipment, and adjacent abnormal equipment with a corresponding distance less than the preset distance are classified into the same density classification group. Each hidden pipe monitoring plot located at the edge of the production plant, determined based on density categorization groups, is detected, and in response, any hidden pipe monitoring plot containing sewage discharge is identified based on the detection results and marked based on the plant's top view.

[0009] Optionally, in the method according to the present invention, obtaining the required wastewater discharge amount corresponding to each production equipment located in the production plant, and obtaining the actual wastewater discharge amount corresponding to each wastewater discharge pipeline located in the production plant and connected to different production equipment based on the discharge monitoring unit, includes: Determine the equipment type corresponding to each production device located in the production plant, where the equipment type includes material drive type and power drive type; The equipment type corresponding to any production equipment is the material-driven type. The real-time moment when the production equipment changes from a stopped production state to a started production state is taken as the starting point for monitoring. Based on the established first monitoring task, the corresponding wastewater discharge amount should be obtained. If the equipment type corresponding to any production equipment is a power drive type, the monitoring should start at any real time when the production equipment starts production, and the corresponding wastewater discharge should be obtained based on the established second monitoring task. The system responds by obtaining the required emissions from any production equipment and, based on the emissions monitoring unit, obtains the actual emissions corresponding to the monitoring task of the wastewater discharge pipeline located in the production plant and connected to that production equipment.

[0010] Optionally, in the method according to the present invention, the equipment type corresponding to any production equipment is a material-driven type, and the real-time moment when the production equipment changes from a stopped production state to a started production state is taken as the starting point for monitoring, and the corresponding wastewater discharge amount is obtained based on the established first monitoring task, including: If any production equipment corresponds to a material-driven type and changes from the start-of-production state to the stop-of-production state at any real-time moment, then that real-time moment is determined as the state start moment. Send a material verification request to the production management terminal of the corresponding production equipment to obtain material verification data and material images; The response determines that the material verification data has the correct attributes based on the material images captured, and establishes a production waiting period with the state start time as the waiting start point and a continuously preset waiting time. When a production device changes from a stopped production state to a started production state at any real time during the production waiting period, that real time is determined as the starting point for monitoring. When a production device changes from a started production state to a stopped production state at any real time after the starting point for monitoring, that real time is determined as the ending point for monitoring. Determine the monitoring period consisting of the starting point and the ending point of the monitoring, and determine the actual material consumption corresponding to the total material production included in the material verification data based on the monitoring period; The required wastewater discharge amount is determined based on the material verification data, including the types of materials produced and the actual amount of materials consumed.

[0011] Optionally, in the method according to the present invention, in response to any production equipment corresponding to a power drive type, taking any real-time moment when the production equipment starts production as the starting point for monitoring, and obtaining the corresponding wastewater discharge amount based on the established second monitoring task, includes: The system responds to the equipment type corresponding to any production equipment as a power drive type, obtains the material production power corresponding to that production equipment, and determines the preset monitoring duration for the corresponding material production power based on preset monitoring values. Take any real-time moment corresponding to the start of production of the production equipment as the starting point for monitoring, and determine the ending point for monitoring that is located after the starting point and corresponds to the preset monitoring duration; The monitoring period is determined by the starting point and the ending point of the monitoring. The production equipment is in the production start state at every real time during the monitoring period, and the preset monitoring quantity is determined as the corresponding wastewater discharge quantity.

[0012] Optionally, in the method according to the invention, in response to obtaining the required emission amount of any production equipment, the actual emission amount corresponding to the monitoring task of the wastewater discharge pipeline located in the production plant and connected to the production equipment is obtained based on the emission monitoring unit, including: The response obtains the required emissions of production equipment of the material-driven type and determines the actual material consumption as emission comparison data. The response obtains the required emissions of production equipment of the corresponding power-driven type and determines the material production power as emission comparison data. Retrieve the delay comparison curve for the corresponding equipment type, and determine the delayed emission duration of the corresponding emission comparison data based on the delay comparison curve; Determine the actual monitoring endpoint that is located after the required monitoring endpoint and corresponds to the delayed emission duration, and determine the actual monitoring period consisting of the required monitoring start point and the required monitoring endpoint; Identify the wastewater discharge pipeline located in the production plant and connected to the production equipment, and control the discharge monitoring unit pre-installed at the end of the wastewater discharge pipeline to collect data during the corresponding actual monitoring period to obtain the actual discharge amount for the corresponding monitoring task.

[0013] Optionally, in the method according to the present invention, in response to determining that the required discharge amount corresponding to the same pipeline connection relationship is greater than the actual discharge amount, the production equipment is identified as abnormal equipment, and adjacent abnormal equipment with corresponding distances less than a preset interval are grouped into the same density group, including: If the response determines that the required discharge volume for the same pipeline connection is greater than the actual discharge volume, the production equipment is identified as abnormal equipment, and the abnormal elements corresponding to each abnormal equipment and the factory area of ​​the corresponding production plant are determined based on the top view of the corresponding production plant. Establish an image coordinate system with the center point of the corresponding factory area as the origin, and divide the top view of the factory into different quadrant directions based on the image coordinate system to obtain the quadrant areas; In response to any anomalous element occupying at least two quadrant regions, determine the proportion of each element in each quadrant region that occupies the anomalous element, and determine that the anomalous element is located in the quadrant region with the largest proportion of the corresponding element. Using the center point of the region as the starting point of the line segment, generate the midpoint line of each quadrant region, and determine the direction of the line segment perpendicular to the midpoint line of the quadrant as the grouping direction; For all abnormal elements located in the same quadrant region, the spacing between corresponding adjacent elements along the grouping direction is determined. In response to any element spacing being less than a preset spacing, all abnormal devices corresponding to that element spacing are assigned to the same density group, resulting in density groups located in different quadrant regions.

[0014] Optionally, in the method according to the invention, each hidden pipe monitoring plot located at the edge of the production plant, determined based on density zoning groups, is detected, and in response to the determination based on the detection results that any hidden pipe monitoring plot contains a sewage discharge pipe, it is marked based on a top view of the corresponding production plant, including: Based on the factory top view, the outline of the external equipment is generated to connect all abnormal equipment located in the same density division group. The first outline tangent line and the second outline tangent line are generated with the center point of the region as the starting point of the line segment. Determine the division region formed by the first contour tangent and the second contour tangent, and generate the division midline of the corresponding division region with the center point of the region as the starting point of the line segment; Obtain the number of anomalies for all abnormal devices in each density division group, and multiply the number of anomalies by a preset conversion coefficient to calculate the delay detection length; The control divider extends to a point outside the factory area and beyond the delay detection length, and generates a detection cutoff line that passes through the endpoint of the dividing divider and is perpendicular to the dividing divider. Control the first contour tangent and the second contour tangent to extend to connect with the detection cutoff line, determine the detection area outside the factory area formed by the first contour tangent and the second contour tangent, and determine the dark pipe monitoring plot at the edge of the production factory based on the detection area; Each site under surveillance for concealed pipes is inspected, and based on the inspection results, if it is determined that any site under surveillance for concealed pipes for sewage discharge exists, the site under surveillance for concealed pipes is marked based on the top view of the corresponding production plant.

[0015] Optionally, in the method according to the invention, each concealed pipe monitoring site is detected, and in response to a determination based on the detection results that a sewage discharge pipe exists in any concealed pipe monitoring site, the concealed pipe monitoring site is marked based on a top view of the corresponding production plant, including: The dividing line is divided into arrays to obtain the array points of each division; Generate detection path lines for each division array point and extending to the first contour tangent and the second contour tangent, wherein each detection path line is perpendicular to the division midline. Each detection path is divided into arrays to obtain each detection path point. Based on the distance between the detection path points and the corresponding factory areas, each detection path is sorted from smallest to largest to obtain the first flight sequence. A first path direction parallel to the detection path line and a second path direction having an inverse relationship with the first path direction are determined. Based on the first flight sequence, all detection path points included in each detection path line corresponding to odd and even numbers are sorted according to the points of the first path direction and the second path direction to obtain the second flight sequence. A flight path is formed by combining the first flight sequence and the second flight sequence, and the UAV is controlled to fly along the flight path. When it arrives at each detection path point, the radar sensing unit pre-set on the UAV is controlled to perform a detection and obtain a detection data. The response determines the presence of a hidden sewage discharge pipe at any detection point based on a single detection data point, and marks the monitoring area of ​​the hidden pipe based on the factory top view of the corresponding production plant. Otherwise, it controls the drone to fly to the area common line of the corresponding factory area for secondary detection.

[0016] Optionally, in the method according to the present invention, controlling the UAV to fly to the area common line of the factory area corresponding to the detection area for secondary detection includes: Determine the common line of the factory area corresponding to the detection area, and control the drone to fly to the center point of the corresponding common line segment; When the drone arrives at the center point of the line segment, the image acquisition unit set on the drone is controlled to acquire images and the edge images of the acquired area are identified. The response determines that there are dry areas in the region edge image based on the recognition results, determines that there are sewage discharge pipes in the region public line, and marks the region public line based on the factory top view of the corresponding production plant.

[0017] According to another aspect of the present invention, a smart water management full-process monitoring system is provided, comprising: The emission acquisition module is configured to acquire the required emission amount of wastewater corresponding to each production equipment located in the production plant, and to acquire the actual emission amount of wastewater corresponding to each wastewater discharge pipeline located in the production plant that has pipeline connection with different production equipment based on the emission monitoring unit. The anomaly determination module is configured to determine that if the required emission amount for the same pipeline connection is greater than the actual emission amount, the production equipment is identified as an abnormal equipment, and adjacent abnormal equipment with a corresponding distance less than a preset distance are divided into the same density division group. The concealed pipe marking module detects each concealed pipe monitoring plot located at the edge of the production plant, which is determined based on density division groups, and responds by marking the concealed pipe monitoring plot based on the detection results to determine that any concealed pipe has a sewage discharge pipe.

[0018] According to the solution of this invention, the present invention addresses the inherent defects of existing concealed pipe inspection methods that rely on manual searching, such as insufficient detection accuracy, vague inspection scope, and high rates of missed and false detections. Through automated monitoring methods, the accuracy of concealed pipe detection is fundamentally improved, specifically in the following ways: First, this invention calculates the required wastewater discharge for each piece of equipment in a production plant, while simultaneously collecting the actual discharge volume of wastewater discharge pipes connected to the corresponding production equipment. This achieves a precise one-to-one correspondence between the wastewater discharge benchmark and actual discharge data at the level of a single production equipment. It solves the problem of inaccurate source location of abnormal wastewater discharge caused by the rough estimation of total amount and lack of data correspondence in the existing manual investigation mode. It eliminates the inherent defects of vague investigation scope and deviation in investigation direction from the source of data calculation, providing precise targeted guidance for the detection of concealed pipes and fundamentally improving the accuracy of concealed pipe detection. Secondly, this invention directly identifies individual production equipment with abnormal discharge by accurately comparing the required discharge volume with the actual discharge volume corresponding to the same pipeline connection relationship. Then, it clusters adjacent abnormal equipment by spatial distance threshold to form density groups, realizing accurate identification of the spatial distribution characteristics of abnormal discharge equipment. It can quickly locate high-risk concentrated areas of concealed pipe discharge, completely abandoning the blind scanning operation method of the existing manual inspection mode, greatly reducing the target range of concealed pipe detection, effectively avoiding detection blind spots, missed detections and false detections caused by path omissions and excessive inspection range, and further improving the accuracy and detection efficiency of concealed pipe detection. Finally, this invention, based on the density division of abnormal equipment, accurately locates monitoring sites for concealed pipes at the edge of production plants. It can conduct targeted detection only on high-risk target sites and accurately mark them based on the top view of the factory after confirming the sewage discharge concealed pipes. This achieves closed-loop management of the entire process, from accurate judgment of sewage discharge anomalies and targeted location of high-risk areas to accurate detection and marking of concealed pipes. It completely solves the inherent defects of existing manual inspection modes, which rely on experience judgment and have insufficient accuracy and stability of detection results. The entire process does not rely on the subjective experience of the inspectors or the standardization of manual operation. It can stably and efficiently complete the accurate detection and location of concealed pipes, significantly improving the accuracy of concealed pipe detection from the perspective of whole-process management. It provides reliable technical support for the supervision of industrial wastewater compliance and the construction of a smart water affairs closed-loop system. Attached Figure Description

[0019] Figure 1 A flowchart of a smart water management full-process monitoring method according to an embodiment of the present invention is shown; Figure 2 A top view of the factory involved in this embodiment is shown; Figure 3 A structural block diagram of a smart water management full-process monitoring system according to another embodiment of the present invention is shown. Detailed Implementation

[0020] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0021] To address the problems existing in the prior art, the inventors proposed the solution of this invention. One embodiment of this invention provides a method for monitoring the entire process of smart water management. This method can be executed in a computing device, which can be understood as a terminal with data processing capabilities, such as a mobile phone or computer.

[0022] Figure 1 A flowchart of a smart water management full-process monitoring method according to an embodiment of the present invention is shown, such as... Figure 1 As shown, the monitoring method proposed in this embodiment begins with step S1, which includes the following: The required wastewater discharge volume for each production equipment located in the production plant is obtained, and the actual wastewater discharge volume for each wastewater discharge pipeline located in the production plant and connected to different production equipment is obtained based on the discharge monitoring unit.

[0023] For example, in this embodiment, the server can acquire the required wastewater discharge amount for each production device in the production plant. This required discharge amount refers to the total amount of wastewater that a single production device should generate and discharge through a designated path during normal and compliant production. By acquiring the required discharge amount for each production device in the plant individually, the compliant discharge benchmark for each wastewater source can be accurately anchored, avoiding the benchmark ambiguity caused by mixed wastewater discharge data from multiple production devices. This controls the accuracy of data from the wastewater source stage, providing reliable data support for subsequent anomaly detection and fundamentally improving the accuracy of subsequent concealed pipe detection. Furthermore, after completing the acquisition of all required discharge amounts, the server can use emission monitoring... The unit acquires the actual discharge volume of wastewater from each wastewater discharge pipeline within the production plant. This means that the wastewater discharge pipelines have clear connections to different production equipment within the plant, serving as unique channels for receiving wastewater discharged from those equipment. The discharge monitoring unit is a dedicated monitoring device for collecting actual wastewater discharge data. By directly collecting the actual discharge volume from wastewater discharge pipelines with clear connections to production equipment, it ensures a precise correspondence between the collected actual discharge volume and the previously acquired expected discharge volume. This guarantees a complete match between the two sets of data sources used for comparison, effectively avoiding errors in anomaly judgments due to data discrepancies, reducing the scope of invalid investigations in subsequent concealed pipe detection, making the target area for concealed pipe detection more targeted, and further improving the overall accuracy of concealed pipe detection.

[0024] Furthermore, in this embodiment, the aforementioned "obtaining the required wastewater discharge amount corresponding to each production equipment located in the production plant, and obtaining the actual wastewater discharge amount corresponding to each wastewater discharge pipeline located in the production plant and connected to different production equipment based on the discharge monitoring unit" may further include the following steps: Determine the equipment type corresponding to each production device located in the production plant, where the equipment type includes material drive type and power drive type; The equipment type corresponding to any production equipment is the material-driven type. The real-time moment when the production equipment changes from a stopped production state to a started production state is taken as the starting point for monitoring. Based on the established first monitoring task, the corresponding wastewater discharge amount should be obtained. If the equipment type corresponding to any production equipment is a power drive type, the monitoring should start at any real time when the production equipment starts production, and the corresponding wastewater discharge should be obtained based on the established second monitoring task. The system responds by obtaining the required emissions from any production equipment and, based on the emissions monitoring unit, obtains the actual emissions corresponding to the monitoring task of the wastewater discharge pipeline located in the production plant and connected to that production equipment.

[0025] For example, in this embodiment, the required emission amount and the actual emission amount can be specifically implemented based on the following method steps: First, the server can determine the type of equipment for each production device in the production plant. The equipment types include material-driven types and power-driven types. By classifying each production device in the plant, the server can establish corresponding monitoring logic to match the discharge characteristics of different types of production devices. This avoids the deviation in discharge calculation caused by using a uniform standard, improves the accuracy of data from the source of data calculation, and lays a reliable foundation for subsequent anomaly judgment and hidden pipe detection. Next, the server will perform the corresponding operation to obtain the required discharge amount for different types of production equipment. When the server determines that the equipment type corresponding to any production equipment is material-driven, it will take the real time when the production equipment changes from a stopped production state to a started production state as the monitoring starting point. Based on the established first monitoring task, the required discharge amount of the corresponding wastewater will be obtained. Here, by setting the monitoring starting point by matching the sewage discharge characteristics of the material-driven production equipment, the complete production sewage discharge cycle of this type of equipment can be accurately locked, ensuring the completeness and accuracy of the required discharge amount calculation, avoiding the distortion of calculation data caused by misaligned monitoring periods, and further improving the reliability of subsequent anomaly judgment. Meanwhile, when the server determines that any production equipment is a power-driven type, it takes any real-time moment when the production equipment starts production as the starting point for monitoring. Based on the established second monitoring task, it obtains the corresponding wastewater discharge amount. Here, by setting a monitoring starting point that is adapted to the discharge characteristics of the power-driven type of production equipment, it can accurately match the discharge pattern of this type of equipment, ensuring that the calculation result of the discharge amount is completely consistent with the actual production discharge situation of the equipment. There will be no calculation deviation that does not match the discharge characteristics of the equipment, so that the discharge amount of each production equipment has accurate reference value, effectively avoiding abnormal misjudgments caused by the mismatch of the calculation standard, and greatly reducing the scope of invalid investigation of subsequent hidden pipe detection. Finally, after the server obtains the required emission amount of any production equipment, it will obtain the actual emission amount of the corresponding monitoring task of the sewage discharge pipeline located in the production plant and connected to the production equipment based on the emission monitoring unit. It can be explained that by matching the actual emission amount with the corresponding monitoring task, the actual emission amount can be made to correspond with the previously calculated required emission amount, ensuring that the two sets of data used for comparison are completely matched in monitoring logic and monitoring period, completely avoiding the comparison deviation caused by data misalignment, making the judgment result of abnormal equipment more accurate and reliable, and effectively improving the overall accuracy of hidden pipe detection.

[0026] It can be explained that, in this embodiment, the material-driven type can be understood as wastewater discharge volume primarily determined by the material consumption and / or total processing volume during the production process, and not directly linearly related to the equipment's own operating power. The discharge volume changes synchronously with the material processing volume. Examples include fruit and vegetable and / or meat washing machines, industrial reaction kettles, and electroplating tanks. Conversely, the power-driven type can be understood as wastewater discharge volume primarily determined by the equipment's operating power and effective operating time, and not directly linearly related to the material consumption and / or total processing volume during the production process. The discharge volume changes synchronously with the equipment's operating time and operating power. Examples include aeration fans, wet spray dust removal fans, CNC machine tool cooling circulation units, and pure water preparation reverse osmosis units. Furthermore, in this embodiment, the aforementioned "responding to any production equipment corresponding to a material-driven type, taking the real-time moment when the production equipment changes from a stopped production state to a started production state as the starting point for monitoring, and obtaining the corresponding wastewater discharge amount based on the established first monitoring task" may also include the following steps: If any production equipment corresponds to a material-driven type and changes from the start-of-production state to the stop-of-production state at any real-time moment, then that real-time moment is determined as the state start moment. Send a material verification request to the production management terminal of the corresponding production equipment to obtain material verification data and material images; The response determines that the material verification data has the correct attributes based on the material images captured, and establishes a production waiting period with the state start time as the waiting start point and a continuously preset waiting time. When a production device changes from a stopped production state to a started production state at any real time during the production waiting period, that real time is determined as the starting point for monitoring. When a production device changes from a started production state to a stopped production state at any real time after the starting point for monitoring, that real time is determined as the ending point for monitoring. Determine the monitoring period consisting of the starting point and the ending point of the monitoring, and determine the actual material consumption corresponding to the total material production included in the material verification data based on the monitoring period; The required wastewater discharge amount is determined based on the material verification data, including the types of materials produced and the actual amount of materials consumed.

[0027] For example, in this embodiment, when the equipment type is material-driven, the emission amount should be implemented based on the following method steps: First, when the server determines that the equipment type corresponding to any production equipment is a material-driven type, and detects that the production equipment changes from the start production state to the stop production state at any real time, the real time can be determined as the state start time. It can be explained that by capturing the production state switching nodes of the production equipment, the key time nodes of the material production batch can be anchored, locking the time benchmark for subsequent material data verification and emission calculation, avoiding the problem of material data mismatch with the production process due to ambiguous time nodes, ensuring the accuracy of emission calculation from the source, and providing reliable benchmark data for subsequent hidden pipe detection. Next, the server will send a material verification request to the production management terminal of the corresponding production equipment to obtain the material verification data and material images of the corresponding production batch. By synchronously obtaining the numerical data and visualized images of the materials, it can provide dual evidence for the authenticity verification of the material data, avoid the problem of emission calculation distortion caused by false material data, effectively ensure the reliability of subsequent anomaly judgment, and reduce the misjudgment of hidden pipe detection due to incorrect baseline data. It can be noted that the material images can be obtained by taking pictures of the materials on the production management terminal. Here, the production management terminal can be a terminal for the production plant, such as a mobile phone or computer. Subsequently, the server verifies the material verification data based on the acquired material images. When the material verification data is determined to be correct based on the material images, for example, when the material production type and total production volume are consistent with the identification content obtained from the material images, the server determines that the material verification data has correct attributes. Furthermore, it establishes a production waiting period with the state start time as the starting point and a continuously preset waiting time. It can be explained that by setting a fixed production waiting period, the successive production cycle of the same material batch can be defined, avoiding the situation where the factory changes materials due to excessively long production intervals, which would lead to a mismatch between material data and the actual production process. This ensures the consistency between the subsequent monitoring process and the corresponding material batch, and further improves the accuracy of emission calculation. Then, the server continuously monitors the operating status of the production equipment. When the production equipment changes from a stopped production state to a started production state at any real time during the production waiting period, that real time is determined as the starting point for monitoring. At the same time, the server continuously monitors the subsequent operating status of the production equipment. When the production equipment changes from a started production state to a stopped production state at any real time after the starting point for monitoring, that real time is determined as the ending point for monitoring. Here, by locking the starting point and ending point for monitoring the subsequent production during the production waiting period, it can be ensured that the monitored production process completely corresponds to the same batch of materials that have been verified before, completely avoiding the misalignment of the accounting benchmark caused by material changes, and ensuring that the subsequent emission calculations completely match the actual material production situation, without the problem of data being disconnected from the production process. Then, the server can determine the monitoring period consisting of the starting point and the ending point of the monitoring period, and determine the actual material consumption corresponding to the total material production included in the material verification data based on the monitoring period. That is, by locking the complete production monitoring period to calculate the actual material consumption, the total amount of material actually involved in production within the production cycle can be accurately obtained, avoiding the deviation in material consumption calculation caused by incomplete production periods, and ensuring that the material consumption data fully matches the actual production situation, providing an accurate core basis for emission calculation. Finally, the server can determine the required wastewater discharge amount for the corresponding production equipment based on the material production types included in the material verification data and the calculated material consumption amount. In this embodiment, by matching the discharge characteristics of the corresponding material production types with the accurate material consumption amount to calculate the required discharge amount, the calculated required discharge amount can be made to fully match the compliant discharge situation of the production equipment in the corresponding production cycle. This completely avoids the discharge amount calculation deviation caused by material data distortion, production time misalignment, and material batch change, providing an accurate and reliable compliance benchmark for subsequent abnormal equipment identification. It effectively avoids abnormal misjudgments caused by incorrect benchmark data, significantly reduces the scope of invalid investigations in subsequent concealed pipe detection, and improves the accuracy of concealed pipe detection from the core data level.

[0028] Furthermore, in this embodiment, the aforementioned "responding to any production equipment corresponding to a power drive type, taking any real-time moment when the production equipment starts production as the starting point for monitoring, and obtaining the corresponding wastewater discharge amount based on the established second monitoring task" may also include the following steps: The system responds to the equipment type corresponding to any production equipment as a power drive type, obtains the material production power corresponding to that production equipment, and determines the preset monitoring duration for the corresponding material production power based on preset monitoring values. Take any real-time moment corresponding to the start of production of the production equipment as the starting point for monitoring, and determine the ending point for monitoring that is located after the starting point and corresponds to the preset monitoring duration; The monitoring period is determined by the starting point and the ending point of the monitoring. The production equipment is in the production start state at every real time during the monitoring period, and the preset monitoring quantity is determined as the corresponding wastewater discharge quantity.

[0029] For example, in this embodiment, when the device type is power-driven, the emission amount should be specifically implemented based on the following method steps: First, when the server determines that the equipment type corresponding to any production equipment is a power drive type, it can obtain the material production power of the corresponding production equipment. It can be noted that the material production power involved in this embodiment is the core operating power parameter when the production equipment is carrying out production operations. By accurately obtaining the material production power of the equipment, the core indicators related to pollution discharge during the production operation of the equipment can be anchored, providing an accurate matching basis for the setting of subsequent monitoring duration, avoiding the deviation in emission calculation caused by the mismatch between the monitoring duration and the actual operating capacity of the equipment, ensuring the rationality of the emission calculation from the source, and providing reliable benchmark data for subsequent anomaly judgment and hidden pipe detection. Next, the server will determine the preset monitoring duration for the corresponding material production power based on the preset monitoring quantity. The preset monitoring quantity is the compliant emission benchmark value adapted to the production equipment driven by this power type. By matching the monitoring duration of the corresponding material production power with the preset monitoring quantity as the benchmark, it can ensure that the final determined emission amount has a sufficient numerical volume, avoiding the situation where the emission amount is too small due to unreasonable monitoring duration settings, making it impossible to clearly identify abnormal gaps in emission amount. This lays a solid foundation for the accurate judgment of subsequent abnormal situations, effectively reduces the probability of missed abnormal judgments, and thus improves the accuracy of subsequent hidden pipe detection. Subsequently, the server will take any real-time moment when the production equipment starts production as the starting point for monitoring, and at the same time determine the ending point for monitoring that is after the starting point and corresponds to the preset monitoring duration. It can be explained that by accurately locking the start and end points of monitoring by matching the preset monitoring duration, the integrity and consistency of the monitoring period can be guaranteed, avoiding the instability of the emission calculation benchmark caused by arbitrary changes in the monitoring period, and providing a unified and standardized reference standard for the calculation process of the emission amount, thereby further improving the reliability and consistency of the calculation results. Finally, the server determines the monitoring period consisting of the starting point and the ending point of the monitoring, and continuously monitors the real-time production status of the production equipment during the monitoring period. When the production equipment is detected to be in a production state at every real-time moment during the monitoring period, the preset monitoring quantity can be determined as the corresponding wastewater discharge quantity. It can be explained that by using the preset monitoring quantity as the discharge quantity only when the equipment maintains a stable production state throughout the entire period, it can be ensured that the calculated discharge quantity fully matches the compliant sewage discharge under continuous and stable production. At the same time, relying on the sufficient numerical volume of the preset monitoring quantity, the deviation between the discharge quantity and the actual discharge quantity can be more clearly identified, which greatly improves the accuracy of abnormal equipment judgment, effectively avoids abnormal misjudgment and missed judgment due to unclear sewage discharge gaps, and significantly reduces the scope of invalid investigation of subsequent concealed pipe detection, thereby improving the overall accuracy of concealed pipe detection from the core benchmark data level.

[0030] Furthermore, in this embodiment, the aforementioned "responding to obtain the required emission amount of any production equipment, and obtaining the actual emission amount of the wastewater discharge pipeline located in the production plant and connected to the production equipment based on the emission monitoring unit" may further include the following steps: The response obtains the required emissions of production equipment of the material-driven type and determines the actual material consumption as emission comparison data. The response obtains the required emissions of production equipment of the corresponding power-driven type and determines the material production power as emission comparison data. Retrieve the delay comparison curve for the corresponding equipment type, and determine the delayed emission duration of the corresponding emission comparison data based on the delay comparison curve; Determine the actual monitoring endpoint that is located after the required monitoring endpoint and corresponds to the delayed emission duration, and determine the actual monitoring period consisting of the required monitoring start point and the required monitoring endpoint; Identify the wastewater discharge pipeline located in the production plant and connected to the production equipment, and control the discharge monitoring unit pre-installed at the end of the wastewater discharge pipeline to collect data during the corresponding actual monitoring period to obtain the actual discharge amount for the corresponding monitoring task.

[0031] For example, in this embodiment, the actual emissions can be achieved based on the following method steps: First, after obtaining the required emissions from any production equipment, the server performs a corresponding emissions comparison data determination operation based on the equipment type. Specifically, when the server obtains the required emissions from a material-driven production equipment, it determines the actual material consumption as the emissions comparison data. Conversely, when the server obtains the required emissions from a power-driven production equipment, it determines the material production power as the emissions comparison data. In other words, based on the material-driven type, determining emissions comparison data by matching the pollution-related indicators of material-driven production equipment can... Precisely identifying the core parameters affecting the wastewater discharge patterns of this type of equipment provides an accurate matching basis for the subsequent calculation of delayed discharge duration, avoiding calculation deviations caused by mismatched core parameters. This ensures the rationality of subsequent actual discharge data collection from a fundamental perspective. For power drive types, matching the wastewater discharge correlation indicators of power drive production equipment as emission comparison data can fully adapt to the wastewater discharge characteristics of this type of equipment, ensuring that the subsequent calculation of delayed discharge duration is completely consistent with the actual operation and wastewater discharge of the equipment, without any calculation errors due to mismatch with the equipment type, further ensuring the accuracy of subsequent data collection. Next, the server can retrieve the corresponding delay comparison curve based on the equipment type. It can be explained that the delay comparison curve is a pre-calibrated benchmark curve adapted to the wastewater discharge delay characteristics of the corresponding equipment type. By retrieving the delay comparison curve that is precisely matched to the equipment type, it can be ensured that the calculation of the delayed discharge time is fully adapted to the sewage discharge pattern of the corresponding equipment, avoiding the time calculation deviation caused by using a uniform reference standard. At the same time, the server can determine the delayed discharge time of the corresponding discharge comparison data based on the delay comparison curve, which can accurately match the actual operating status of the current production equipment, lock the complete time cycle of wastewater from the production equipment to the end of the corresponding pipeline, avoid the problem of incomplete collection of actual discharge volume due to failure to consider the wastewater discharge delay characteristics, and effectively prevent the distortion of the comparison between the expected discharge volume and the actual discharge volume caused by missing collected data. Subsequently, the server can determine the actual monitoring endpoint located after the endpoint that should be monitored and corresponding to the delayed discharge duration. At the same time, it can determine the actual monitoring period consisting of the starting point and the endpoint that should be monitored. Here, by first clarifying the actual monitoring endpoint that covers the delayed discharge cycle of sewage, and then locking the complete actual monitoring period, the full-cycle data collection range of the corresponding production equipment's sewage discharge behavior can be defined. This ensures that the collection period can completely cover the entire process of sewage generation and discharge, avoiding the problem of incomplete data collection due to unreasonable monitoring period settings, and laying a solid foundation for subsequent accurate discharge comparison. Finally, the server identifies the wastewater discharge pipeline located in the production plant that is connected to the production equipment. This ensures that the collected actual discharge volume completely corresponds to the wastewater discharge situation of the target production equipment, avoiding data misalignment caused by pipeline and equipment mismatch. Simultaneously, it controls the discharge monitoring unit pre-set at the end of the wastewater discharge pipeline to collect data during the corresponding actual monitoring period, obtaining the actual discharge volume for the corresponding monitoring task. It can be noted that this embodiment, by conducting data collection within the complete actual monitoring period that matches the wastewater discharge delay characteristics, can completely obtain all wastewater discharge volume generated under the corresponding monitoring task of the production equipment. There will be no situation where some wastewater data is not collected due to discharge delay, ensuring that the actual discharge volume collection results completely match the actual wastewater discharge situation of the equipment. This guarantees the accuracy and reliability of the comparison results between the expected discharge volume and the actual discharge volume, effectively avoiding the omission of abnormal equipment due to incomplete data collection, significantly reducing the scope of invalid investigation in subsequent concealed pipe detection, and improving the overall accuracy of concealed pipe detection from the core data comparison stage.

[0032] It should be noted that the discharge monitoring unit involved in this embodiment can specifically adopt existing technologies such as ultrasonic wastewater discharge monitoring units, electromagnetic wastewater discharge monitoring units, and / or weir-type wastewater discharge monitoring units. These units can be pre-installed at the end of the wastewater discharge pipeline to collect the actual discharge flow rate of wastewater during the corresponding monitoring period. Step S2 includes the following: If the response determines that the required emission amount for the same pipeline connection is greater than the actual emission amount, the production equipment is identified as abnormal equipment, and adjacent abnormal equipment with a corresponding distance less than the preset distance are assigned to the same density group.

[0033] For example, in this embodiment, after the server completes the acquisition of the required wastewater discharge volume corresponding to each production equipment in the production plant and the actual wastewater discharge volume corresponding to each wastewater discharge pipeline connected to the production equipment based on step S1, it can further compare and verify the required discharge volume and the actual discharge volume corresponding to the same pipeline connection. It can be explained that by comparing two sets of data under the same pipeline connection, the judgment deviation caused by data mismatch can be completely avoided, ensuring the accuracy and reliability of the anomaly judgment result, and laying a solid data foundation for subsequent concealed pipe detection. Among them, when the server determines that the required discharge volume corresponding to the same pipeline connection is greater than the actual discharge volume, In this way, the production equipment is identified as abnormal, which allows for direct identification of the equipment with compliance gaps in wastewater discharge. This pinpoints the source of abnormal wastewater discharge risks, significantly narrowing the scope of subsequent concealed pipe inspections and improving the accuracy of concealed pipe detection from the source. Simultaneously, the server can confirm the adjacent spacing between each abnormal device and group those abnormal devices with adjacent spacing smaller than a preset distance into the same density group. This centralized classification method can accurately identify the concentrated distribution area of ​​abnormal wastewater discharge equipment, avoiding the detection omissions and inefficiencies caused by decentralized blind inspections. This makes the target area for subsequent concealed pipe inspections more focused, further improving the accuracy and overall efficiency of concealed pipe detection.

[0034] Furthermore, in this embodiment, the aforementioned "response determining that the required emission amount corresponding to the same pipeline connection relationship is greater than the actual emission amount, identifying the production equipment as abnormal equipment, and classifying adjacent abnormal equipment with corresponding distances less than a preset interval into the same density group" may also include the following steps: If the response determines that the required discharge volume for the same pipeline connection is greater than the actual discharge volume, the production equipment is identified as abnormal equipment, and the abnormal elements corresponding to each abnormal equipment and the factory area of ​​the corresponding production plant are determined based on the top view of the corresponding production plant. Establish an image coordinate system with the center point of the corresponding factory area as the origin, and divide the top view of the factory into different quadrant directions based on the image coordinate system to obtain the quadrant areas; In response to any anomalous element occupying at least two quadrant regions, determine the proportion of each element in each quadrant region that occupies the anomalous element, and determine that the anomalous element is located in the quadrant region with the largest proportion of the corresponding element. Using the center point of the region as the starting point of the line segment, generate the midpoint line of each quadrant region, and determine the direction of the line segment perpendicular to the midpoint line of the quadrant as the grouping direction; For all abnormal elements located in the same quadrant region, the spacing between corresponding adjacent elements along the grouping direction is determined. In response to any element spacing being less than a preset spacing, all abnormal devices corresponding to that element spacing are assigned to the same density group, resulting in density groups located in different quadrant regions.

[0035] For example, in this embodiment, the acquisition of density division groups can be specifically achieved based on the following method steps: First, when the server determines that the required discharge volume for the same pipeline connection is greater than the actual discharge volume, it identifies the production equipment as abnormal equipment. Furthermore, based on the factory top view of the corresponding production plant, it identifies the abnormal elements of each abnormal equipment and the factory area of ​​the corresponding production plant. It can be explained that the factory top view is a visual view that can fully present the overall spatial layout of the production plant, and the abnormal elements are the visual identifiers of the corresponding abnormal equipment in the factory top view. By converting the abnormal equipment into an abnormal element that can be accurately located within the view, and clarifying the overall factory area of ​​the production plant, a unified spatial reference benchmark can be established for subsequent area division and equipment grouping, avoiding subsequent grouping deviations caused by spatial positioning confusion, and ensuring the accuracy of subsequent concealed pipe detection from the source of spatial positioning. Next, the server will establish an image coordinate system with the center point of the corresponding factory area as the origin, and divide the factory top view into different quadrant directions based on the image coordinate system to obtain each quadrant area. Here, by establishing a unified image coordinate system, a standardized positioning scale can be set for the entire factory area. In addition, by dividing the factory into quadrant directions, the complete factory area can be divided into multiple clearly oriented sub-areas, which can organize and classify abnormal equipment scattered throughout the factory according to spatial orientation, avoiding the management chaos caused by irregular grouping of the entire factory, making the subsequent abnormal equipment grouping work more orderly, and making the area division for subsequent concealed pipe inspection more in line with the actual spatial layout of the factory, which can further improve the accuracy of grouping operations. Subsequently, the server can continuously verify the quadrant affiliation of each abnormal element. When any abnormal element is detected to occupy at least two quadrant regions, the proportion of each element in each quadrant region is determined, and the abnormal element is located in the quadrant region with the largest proportion of the corresponding element. It can be noted that this embodiment determines the unique affiliation quadrant by calculating the proportion of abnormal elements that span multiple quadrants. This ensures that each abnormal device has one and only one clear affiliation region, completely avoiding the grouping confusion caused by an abnormal device belonging to multiple regions at the same time, and also preventing the situation of abnormal devices being missed in classification. This effectively ensures the accuracy of subsequent density grouping and lays a solid foundation for the accurate locking of the dark pipe detection area. Then, the server will generate the midpoint of each quadrant region, with the center point of the region as the starting point of the line segment, and determine the direction of the line segment perpendicular to the midpoint of the quadrant region as the grouping direction. Here, by determining a unified grouping direction based on the central axis of the quadrant, a standardized measurement benchmark can be set for the spacing measurement of adjacent abnormal elements, avoiding the deviation in spacing calculation caused by inconsistent measurement directions, ensuring that the spacing measurement of all adjacent abnormal devices follows the same rule, so that the final grouping judgment result has a unified and reliable reference standard, and there will be no grouping errors caused by different measurement angles. Finally, the server determines the spacing between adjacent elements along the grouping direction for all abnormal elements located in the same quadrant. When any element spacing is determined to be less than a preset spacing, all abnormal devices corresponding to that element spacing are grouped into the same density group. This results in density groups located in different quadrants. It can be noted that this embodiment, by completing spacing measurement and grouping along a unified grouping direction within the same quadrant, can accurately identify clusters of abnormal devices with highly concentrated spatial locations. Grouping abnormal devices with associated abnormal discharge risks into the same group effectively avoids the inefficiency and detection omissions caused by decentralized and indiscriminate detection, making the target area for subsequent concealed pipe detection more focused. At the same time, the density groups obtained by splitting according to quadrants can perfectly match the spatial layout of the production plant, making the area division for concealed pipe detection more scientific and reasonable, significantly reducing the coverage of invalid detections, and effectively improving the accuracy and overall detection efficiency of concealed pipe detection from the core grouping stage.

[0036] Step S3 includes the following: Each hidden pipe monitoring plot located at the edge of the production plant, determined based on density categorization groups, is detected, and in response, any hidden pipe monitoring plot containing sewage discharge is identified based on the detection results and marked based on the plant's top view.

[0037] For example, in this embodiment, after the server completes the density grouping and classification of the corresponding abnormal equipment based on step S2, it can determine each hidden pipe monitoring plot located at the edge of the production plant based on the density group. This means that the hidden pipe monitoring plot is a target plot where there is a risk of abnormal sewage discharge and hidden pipe detection needs to be carried out. By locking the target detection plot based on the density group, the edge location of the factory associated with the concentrated area of ​​abnormal equipment can be directly anchored, avoiding indiscriminate blind inspections of the entire production plant area, significantly reducing the detection coverage, and improving the targeting and accuracy of hidden pipe detection from the very beginning of the detection process. Furthermore, the server can determine the... Each monitoring site for concealed pipes is comprehensively inspected, and the inspection results from each site are received and analyzed in real time. When the inspection results determine that a sewage discharge pipe exists in any monitoring site, the site is marked on the factory top view of the corresponding production plant. By accurately marking the site with the sewage discharge pipe on the factory top view, the specific location of the pipe can be clearly and intuitively located, making the inspection results traceable and viewable. At the same time, the distribution area of ​​the pipe is accurately located, further improving the practicality and accuracy of the pipe inspection results and providing accurate and reliable location data for subsequent sewage discharge supervision.

[0038] Furthermore, in this embodiment, the aforementioned "detecting each hidden pipe monitoring plot located at the edge of the production plant based on density division groups, and in response to determining that any hidden pipe monitoring plot has a sewage discharge pipe based on the detection results, marking it based on the top view of the corresponding production plant" may also include the following steps: Based on the factory top view, the outline of the external equipment is generated to connect all abnormal equipment located in the same density division group. The first outline tangent line and the second outline tangent line are generated with the center point of the region as the starting point of the line segment. Determine the division region formed by the first contour tangent and the second contour tangent, and generate the division midline of the corresponding division region with the center point of the region as the starting point of the line segment; Obtain the number of anomalies for all abnormal devices in each density division group, and multiply the number of anomalies by a preset conversion coefficient to calculate the delay detection length; The control divider extends to a point outside the factory area and beyond the delay detection length, and generates a detection cutoff line that passes through the endpoint of the dividing divider and is perpendicular to the dividing divider. Control the first contour tangent and the second contour tangent to extend to connect with the detection cutoff line, determine the detection area outside the factory area formed by the first contour tangent and the second contour tangent, and determine the dark pipe monitoring plot at the edge of the production factory based on the detection area; Each site under surveillance for concealed pipes is inspected, and based on the inspection results, if it is determined that any site under surveillance for concealed pipes for sewage discharge exists, the site under surveillance for concealed pipes is marked based on the top view of the corresponding production plant.

[0039] For example, in this embodiment, the determination and detection of the site for underground pipe monitoring can be achieved based on the following method steps: First, the server can generate an outline of external devices that connect to all abnormal devices in the same density group based on the factory top view of the production plant. By generating an outline of external devices that encompasses all abnormal devices in the same density group, the overall spatial coverage of the abnormal device cluster can be accurately defined, avoiding deviations in the detection area delineation caused by the dispersed location of individual devices. This provides an accurate spatial benchmark for determining the subsequent detection range, improving the rationality of the dark pipe detection area delineation from the source. Next, the server can use the center point of the corresponding factory area as the starting point of the line segment to generate the first contour tangent line and the second contour tangent line that are tangent to the contour of the external equipment. It can be explained that by generating two contour tangent lines that are precisely tangent to the contour of the external equipment, the radiation range of the abnormal equipment cluster towards the outside of the factory can be completely defined, ensuring that the subsequent defined detection area can completely cover all directions in which the abnormal equipment may have hidden pipes, without any detection blind spots, and effectively avoiding the situation of missing hidden pipes. Subsequently, the server will determine the division area formed by the first contour tangent and the second contour tangent, and generate the division midline of the corresponding division area with the center point of the area as the starting point of the line segment. Here, by determining the division area between the two tangents, the core risk location corresponding to the abnormal device cluster can be accurately located. By generating the division midline of the division area, the precise central axis can be determined for the subsequent extension of the detection range, ensuring that the division of the detection area always revolves around the core risk direction of the abnormal device cluster, and there will be no problem of the detection range deviating from the detection area, further improving the targeting of the hidden pipe detection. Then, the server will obtain the number of anomalies of all abnormal devices in each density division group, and multiply the number of anomalies by a preset conversion coefficient to calculate the delay detection length. It can be explained that by matching the number of abnormal devices to calculate the corresponding delay detection length, the extension distance of the detection range can be accurately matched with the level of anomaly risk. The more abnormal devices there are, the higher the risk level, and the longer the corresponding detection extension length. This avoids both missed detections due to high risk but insufficient detection range and invalid detections due to low risk but excessive detection range, thus improving detection accuracy while taking into account detection efficiency. Then, the server can control the extension of the dividing line to be outside the factory area and beyond the delay detection length, and generate a detection cutoff line that passes through the end point of the dividing line and is perpendicular to the dividing line. In this embodiment, by extending the dividing line to be outside the factory area and beyond the calculated delay detection length, it can be ensured that the detection range completely covers the possible path of the dark pipe extending from inside the factory to the outside. By generating a detection cutoff line perpendicular to the dividing line, the outer boundary of the detection range can be accurately locked, so that the delineation of the entire detection area has clear start and end boundaries, avoiding the inefficiency caused by the unlimited expansion of the detection range, while ensuring the complete coverage of the core risk area. Secondly, the server controls the first and second contour tangents to extend to connect with the detection cutoff line, determining the detection area outside the factory area formed by the first and second contour tangents. Based on the detection area, the server determines the hidden pipe monitoring site at the edge of the production factory. It can be explained that by extending the two contour tangents to connect with the detection cutoff line, a closed detection area can be formed. The detection area can fit the risk radiation direction of the abnormal equipment cluster, and accurately correspond to the edge position of the production factory. Based on the detection area, the server determines the hidden pipe monitoring site, which can focus the final detection target site on the edge area of ​​the high-risk production factory, and concentrate the detection resources on the area most likely to have hidden pipes, fundamentally improving the accuracy and detection rate of hidden pipe detection. Finally, the server will detect each identified concealed pipe monitoring site and respond based on the detection results to determine if any concealed pipe for sewage discharge exists in any of the monitoring sites. The server will then mark the monitoring site based on the factory top view of the corresponding production plant. It can be seen that by conducting targeted detection of concealed pipe monitoring sites, the detection efficiency and accuracy of concealed pipes can be greatly improved. At the same time, marking the sites with concealed pipes on the factory top view makes the specific location of the concealed pipes intuitively visible, providing accurate and reliable location data for subsequent supervision and disposal work, and further improving the practicality and accuracy of the entire process monitoring of smart water management.

[0040] In one example, Figure 2 A top view of the factory involved in this embodiment is shown, wherein, as Figure 2 As shown, the factory area is Figure 2 In the rectangular area of ​​the factory area, there are abnormal elements a and b in one of the quadrants of the factory area. It can be seen that, based on the top view of the factory, this embodiment generates a first contour tangent line Q1 and a second contour tangent line Q2, a dividing line Z and a detection cutoff line J that are tangent to abnormal elements a and b. The area located at the edge of the factory area and corresponding to the area composed of the detection cutoff line J, the first contour tangent line Q1 and the second contour tangent line Q2 is the detection area.

[0041] Furthermore, in this embodiment, the aforementioned "detecting each hidden pipe monitoring site, and responding to the determination based on the detection results that any hidden pipe monitoring site has a sewage discharge pipe, and marking the hidden pipe monitoring site based on the top view of the corresponding production plant" may also include the following steps: The dividing line is divided into arrays to obtain the array points of each division; Generate detection path lines for each division array point and extending to the first contour tangent and the second contour tangent, wherein each detection path line is perpendicular to the division midline. Each detection path is divided into arrays to obtain each detection path point. Based on the distance between the detection path points and the corresponding factory areas, each detection path is sorted from smallest to largest to obtain the first flight sequence. A first path direction parallel to the detection path line and a second path direction having an inverse relationship with the first path direction are determined. Based on the first flight sequence, all detection path points included in each detection path line corresponding to odd and even numbers are sorted according to the points of the first path direction and the second path direction to obtain the second flight sequence. A flight path is formed by combining the first flight sequence and the second flight sequence, and the UAV is controlled to fly along the flight path. When it arrives at each detection path point, the radar sensing unit pre-set on the UAV is controlled to perform a detection and obtain a detection data. The response determines the presence of a hidden sewage discharge pipe at any detection point based on a single detection data point, and marks the monitoring area of ​​the hidden pipe based on the factory top view of the corresponding production plant. Otherwise, it controls the drone to fly to the area common line of the corresponding factory area for secondary detection.

[0042] For example, in this embodiment, a single detection of a site for monitoring concealed pipes can be implemented based on the following method steps: First, in order to provide uniformly distributed reference nodes for the subsequent deployment of detection paths and avoid uneven density of detection paths, the server can divide the dividing lines into arrays to obtain each array point. This ensures comprehensive coverage of the detection range from the source of path planning, effectively preventing detection blind spots and improving the integrity and accuracy of dark pipe detection. It can be explained that the array division is performed using a pre-set preset division length, that is, the points are divided sequentially along the extension direction of the detection path to obtain each array point. Next, the server can generate detection path lines that pass through each division array point and extend to the first contour tangent and the second contour tangent. Each detection path line is perpendicular to the division midline. Here, by generating detection path lines perpendicular to the division midline based on the division array points, each detection path line can completely cover the horizontal width of the detection area, ensuring that all deployed detection path lines can completely cover the entire detection area without any uncovered blank areas. This further ensures the detection range has no dead angle coverage, avoids the occurrence of missed detection of dark pipes, and improves the accuracy of dark pipe detection. Subsequently, the server can further divide each detection path into arrays to obtain each detection path point. Based on the distance of the detection path to the corresponding factory area, each detection path is sorted from smallest to largest to obtain the first flight sequence. It can be noted that, similar to the array division of the dividing lines, the uniformly distributed detection path points obtained by arraying each detection path can set precise acquisition nodes for subsequent detection actions, ensuring that every location in the detection area has a corresponding precise detection point, and preventing the omission of hidden pipe signals due to excessively large point intervals. By sorting the detection paths in order of proximity to the factory area from near to far, the detection work can prioritize covering areas closer to the factory area with a higher probability of hidden pipe occurrence, which not only improves the efficiency of the detection work, but also captures hidden pipe signals in high-risk areas as soon as possible, further improving the accuracy and detection efficiency of hidden pipe detection. Then, the server can determine the first path direction parallel to the detection path and the second path direction that is in the opposite direction to the first path direction. Based on the first flight sequence, the server sorts all the detection path points included in each detection path with odd and even numbers according to the first and second path directions to obtain the second flight sequence. It should be noted that by setting opposite detection directions for adjacent detection path lines, the UAV can avoid having to make a long return trip after completing the detection of one detection path before entering the next path. This reduces the invalid flight distance of the UAV and allows the UAV's endurance to be used more in actual detection work. This ensures that the detection work of the entire detection area can be completed continuously in one go without interruption due to insufficient endurance. At the same time, it can also avoid the misalignment of detection points caused by frequent return flights, ensuring that the detection action of each detection path point can be executed accurately. This further improves the accuracy of dark pipe detection from the perspective of the stability of the detection process. Next, the server will generate a flight path composed of a first flight sequence and a second flight sequence, and control the drone to fly along the flight path. When the drone reaches each detection path point, the server will control the radar sensor unit pre-set on the drone to perform a detection and obtain a detection data. Here, the flight path formed by the combination of two sets of flight sequences allows the drone to completely cover all detection path points in the entire detection area according to the planned optimal route, so that the drone presents a Z-shape during the flight. At the same time, the radar sensor unit is activated to carry out detection only when the drone reaches each detection path point, which can significantly reduce the amount of data collected while ensuring the accuracy of the data collection. Finally, when the server determines that a sewage discharge pipe exists at any detection point based on the initial detection data, it will mark the monitored area of ​​the pipe based on the factory top view of the corresponding production plant. Otherwise, it will control the drone to fly to the area common line of the corresponding factory area for secondary detection. That is, this embodiment can intuitively and clearly locate the specific position of the pipe by marking it on the factory top view immediately when a sewage discharge pipe is detected, making the detection results traceable and viewable, and providing accurate positioning basis for subsequent related work. When the sewage discharge pipe is not found in the initial detection, a secondary detection is immediately carried out on the area common line, which can supplement the verification of the boundary where the pipe is most likely to pass through the factory area, avoiding the omission of pipes due to signal interference or slight deviations in the initial detection. This adds double verification guarantee to the pipe detection, further improving the accuracy and reliability of pipe detection from the final stage of result verification.

[0043] It can be explained that marking on the factory's view can be understood as visually marking the detection area corresponding to the underground pipe monitoring plot based on pixel marking or symbol marking, in order to improve the identifiability of the corresponding detection area.

[0044] Furthermore, in this embodiment, the aforementioned "controlling the drone to fly to the public line of the factory area corresponding to the detection area for secondary detection" may also include the following steps: Determine the common line of the factory area corresponding to the detection area, and control the drone to fly to the center point of the corresponding common line segment; When the drone arrives at the center point of the line segment, the image acquisition unit set on the drone is controlled to acquire images and the edge images of the acquired area are identified. The response determines that there are dry areas in the region edge image based on the recognition results, determines that there are sewage discharge pipes in the region public line, and marks the region public line based on the factory top view of the corresponding production plant.

[0045] For example, in this embodiment, the secondary detection of the regional public line can be implemented based on the following method steps: First, when the server conducts secondary testing in the corresponding testing area, it can determine the common line of the corresponding factory area. Here, the common line can be understood as the boundary between the internal and external areas of the production factory, and it is also the key location where sewage discharge pipes are most likely to extend from the inside of the factory to the outside. By locking the common line, the scope of secondary testing can be directly focused on the boundary location where the pipes are most likely to appear, avoiding ineffective testing in areas without risk. This greatly improves the targeting and accuracy of the testing from the very beginning of the secondary testing. Next, the server will control the drone to fly to the center point of the corresponding area's common line segment. It can be explained that selecting the center point of the area's common line segment as the drone's detection operation position allows the drone to be in the best observation position that can completely cover the entire area's common line. This ensures that the subsequent image acquisition work can completely cover the entire boundary area without any blind spots at the two ends of the boundary, effectively guaranteeing the coverage integrity of the secondary inspection. It can also avoid the situation of missing dark pipes due to improper observation position, further improving the comprehensiveness of the inspection. Subsequently, when the server detects that the drone has reached the center point of the line segment of the public line, it will control the image acquisition unit pre-set on the drone to carry out image acquisition work to obtain the area edge image of the corresponding public line. Then, the server will identify the acquired regional edge images. Specifically, it can input the images into a pre-trained dryness recognition model to determine whether there are dry areas in the regional edge images. The dryness recognition model can be obtained based on a neural network learning module or a machine learning model. Finally, when the server determines that a dried-up area exists in the regional edge image based on the recognition results, it will determine that there is a sewage discharge pipe in the common line of that area. At the same time, the common line of that area is marked based on the factory top view of the corresponding production plant. It can be explained that, because sewage discharge pipes may form dried-up traces around the discharge outlet at the factory boundary that are different from normal ground during the sewage discharge process, identifying the dried-up area to determine the existence of the pipe can complete the accurate verification of the pipe from the dimension of ground traces. This effectively makes up for the detection loopholes that may exist in single detection methods, and provides intuitive and reliable physical evidence for determining the existence of pipes, greatly improving the accuracy and detection rate of pipe detection. At the same time, marking the common line of the area where the sewage discharge pipe exists on the factory top view can intuitively and clearly locate the specific distribution location of the pipe, making the detection results traceable and viewable, and providing accurate location basis for subsequent sewage discharge supervision and disposal work.

[0046] In summary, according to the solution of this embodiment, this embodiment addresses the inherent defects of existing concealed pipe inspection methods that rely on manual searching, such as insufficient detection accuracy, vague inspection scope, and high rates of missed and false detections. By employing automated monitoring methods, it fundamentally improves the accuracy of concealed pipe detection. Specifically, this is reflected in the following aspects: First, this embodiment calculates the required wastewater discharge volume for each production equipment in the production plant, and accurately collects the actual discharge volume of wastewater discharge pipelines connected to the corresponding production equipment. This achieves a precise one-to-one correspondence between the discharge benchmark and actual discharge data at the level of a single production equipment. It solves the problem of inaccurate location of abnormal discharge sources caused by the rough estimation of total amount and lack of data correspondence in the existing manual investigation mode. It eliminates the inherent defects of vague investigation scope and deviation in investigation direction from the source link of data calculation, and provides precise targeted guidance for the detection of hidden pipes, fundamentally improving the accuracy of hidden pipe detection. Secondly, this embodiment directly identifies individual production equipment with abnormal discharge by accurately comparing the required discharge volume with the actual discharge volume corresponding to the same pipeline connection relationship. Then, it clusters adjacent abnormal equipment by spatial distance threshold to form density division groups, realizing accurate identification of the spatial distribution characteristics of abnormal discharge equipment. It can quickly locate high-risk concentrated areas of concealed pipe discharge, completely abandoning the blind scanning operation method of the existing manual inspection mode, greatly reducing the target range of concealed pipe detection, effectively avoiding detection blind spots, missed detection and false detection caused by path omission and excessive inspection range, and further improving the accuracy and detection efficiency of concealed pipe detection. Finally, this embodiment accurately locates monitoring sites for concealed pipes at the edge of the production plant based on the density division of abnormal equipment. It can conduct targeted detection only on high-risk target sites, and accurately mark them based on the top view of the plant after confirming the sewage discharge concealed pipes. This realizes a closed-loop management system for the entire process, from accurate judgment of sewage discharge anomalies and targeted location of high-risk areas to accurate detection and marking of concealed pipes. It completely solves the inherent defects of the existing manual inspection mode, which relies on experience judgment and has insufficient accuracy and stability of detection results. The entire process does not rely on the subjective experience of the inspectors or the standardization of manual operation. It can stably and efficiently complete the accurate detection and location of concealed pipes, significantly improving the accuracy of concealed pipe detection from the perspective of the whole process management. It provides reliable technical support for the supervision of industrial wastewater compliance and the construction of a smart water affairs closed-loop system.

[0047] Another embodiment of the present invention provides a smart water management full-process monitoring system. Figure 3 Its corresponding system block diagram includes: The emission acquisition module is configured to acquire the required emission amount of wastewater corresponding to each production equipment located in the production plant, and to acquire the actual emission amount of wastewater corresponding to each wastewater discharge pipeline located in the production plant that has pipeline connection with different production equipment based on the emission monitoring unit. The anomaly determination module is configured to determine that if the required emission amount for the same pipeline connection is greater than the actual emission amount, the production equipment is identified as an abnormal equipment, and adjacent abnormal equipment with a corresponding distance less than a preset distance are divided into the same density division group. The concealed pipe marking module detects each concealed pipe monitoring plot located at the edge of the production plant, which is determined based on density division groups, and responds by marking the concealed pipe monitoring plot based on the detection results to determine that any concealed pipe has a sewage discharge pipe.

[0048] In the specification provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used with the examples of this invention. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing preferred embodiments of the invention.

[0049] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0050] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various aspects of the invention, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof.

[0051] Those skilled in the art will understand that modules, units, or components of the devices disclosed in the examples herein can be arranged in the devices described in this embodiment, or alternatively, can be located in one or more devices different from the devices in this example. The modules in the foregoing examples can be combined into a single module or, in addition, can be divided into multiple sub-modules.

[0052] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components.

[0053] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments.

[0054] Furthermore, some of the embodiments described herein are methods or combinations of method elements that can be implemented by a processor of a computer system or by other means of performing the functions. Therefore, a processor having the necessary instructions for implementing the methods or method elements forms means for implementing the methods or method elements. Furthermore, the elements described herein in the apparatus embodiments are examples of means for implementing the functions performed by elements for the purposes of carrying out the invention.

[0055] As used herein, unless otherwise specified, the use of ordinal numbers such as “first,” “second,” “third,” etc., to describe ordinary objects merely indicates different instances of similar objects and is not intended to imply that the objects being described must have a given order in time, space, ordering, or any other manner.

[0056] Although the invention has been described with respect to a limited number of embodiments, those skilled in the art will understand from the foregoing description that other embodiments are conceivable within the scope of the invention described herein. Furthermore, it should be noted that the language used in this specification has been chosen primarily for readability and edibility purposes, and not for the purpose of explaining or limiting the subject matter of the invention.

Claims

1. A smart water whole-process monitoring method, characterized in that, include: Obtain the required wastewater discharge amount for each production equipment located in the production plant, and obtain the actual wastewater discharge amount for each wastewater discharge pipeline located in the production plant that has pipeline connection with different production equipment based on the discharge monitoring unit; If the response determines that the required emission amount corresponding to the same pipeline connection is greater than the actual emission amount, the production equipment is identified as abnormal equipment, and adjacent abnormal equipment with a corresponding distance less than the preset distance are classified into the same density classification group. Each hidden pipe monitoring plot located at the edge of the production plant, determined based on density categorization groups, is detected, and in response, any hidden pipe monitoring plot containing sewage discharge is identified based on the detection results and marked based on the plant's top view.

2. The method according to claim 1, characterized in that, Obtain the required wastewater discharge volume for each production equipment located in the production plant, and based on the discharge monitoring unit, obtain the actual wastewater discharge volume for each wastewater discharge pipeline located in the production plant that has pipeline connections to different production equipment, including: Determine the equipment type corresponding to each production device located in the production plant, where the equipment type includes material drive type and power drive type; The equipment type corresponding to any production equipment is the material-driven type. The real-time moment when the production equipment changes from a stopped production state to a started production state is taken as the starting point for monitoring. Based on the established first monitoring task, the corresponding wastewater discharge amount should be obtained. If the equipment type corresponding to any production equipment is a power drive type, the monitoring should start at any real time when the production equipment starts production, and the corresponding wastewater discharge should be obtained based on the established second monitoring task. The system responds by obtaining the required emissions from any production equipment and, based on the emissions monitoring unit, obtains the actual emissions corresponding to the monitoring task of the wastewater discharge pipeline located in the production plant and connected to that production equipment.

3. The method according to claim 2, characterized in that, The response equipment type corresponding to any production equipment is a material-driven type. The real-time moment when the production equipment transitions from a stopped production state to a started production state is taken as the monitoring starting point. Based on the established first monitoring task, the corresponding wastewater discharge amount is obtained, including: If any production equipment corresponds to a material-driven type and changes from the start-of-production state to the stop-of-production state at any real-time moment, then that real-time moment is determined as the state start moment. Send a material verification request to the production management terminal of the corresponding production equipment to obtain material verification data and material images; The response determines that the material verification data has the correct attributes based on the material images captured, and establishes a production waiting period with the state start time as the waiting start point and a continuously preset waiting time. When a production device changes from a stopped production state to a started production state at any real time during the production waiting period, that real time is determined as the starting point for monitoring. When a production device changes from a started production state to a stopped production state at any real time after the starting point for monitoring, that real time is determined as the ending point for monitoring. Determine the monitoring period consisting of the starting point and the ending point of the monitoring, and determine the actual material consumption corresponding to the total material production included in the material verification data based on the monitoring period; The required wastewater discharge amount is determined based on the material verification data, including the types of materials produced and the actual amount of materials consumed.

4. The method according to claim 2, characterized in that, The response indicates that the equipment type corresponding to any production equipment is a power-driven type. Taking any real-time moment when the production equipment begins production as the starting point for monitoring, the corresponding wastewater discharge amount is obtained based on the established second monitoring task, including: The system responds to the equipment type corresponding to any production equipment as a power drive type, obtains the material production power corresponding to that production equipment, and determines the preset monitoring duration for the corresponding material production power based on preset monitoring values. Take any real-time moment corresponding to the start of production of the production equipment as the starting point for monitoring, and determine the ending point for monitoring that is located after the starting point and corresponds to the preset monitoring duration; The monitoring period is determined by the starting point and the ending point of the monitoring. The production equipment is in the production start state at every real time during the monitoring period, and the preset monitoring quantity is determined as the corresponding wastewater discharge quantity.

5. The method according to claim 3 or 4, characterized in that, The response obtains the required emissions from any production equipment, and based on the emission monitoring unit, obtains the actual emissions corresponding to the monitoring task of the wastewater discharge pipeline located in the production plant and connected to the production equipment, including: The response obtains the required emissions of production equipment of the material-driven type and determines the actual material consumption as emission comparison data. The response obtains the required emissions of production equipment of the corresponding power-driven type and determines the material production power as emission comparison data. Retrieve the delay comparison curve for the corresponding equipment type, and determine the delayed emission duration of the corresponding emission comparison data based on the delay comparison curve; Determine the actual monitoring endpoint that is located after the required monitoring endpoint and corresponds to the delayed emission duration, and determine the actual monitoring period consisting of the required monitoring start point and the required monitoring endpoint; Identify the wastewater discharge pipeline located in the production plant and connected to the production equipment, and control the discharge monitoring unit pre-installed at the end of the wastewater discharge pipeline to collect data during the corresponding actual monitoring period to obtain the actual discharge amount for the corresponding monitoring task.

6. The method according to claim 1, characterized in that, If the response determines that the required emission amount corresponding to the same pipeline connection is greater than the actual emission amount, the production equipment is identified as abnormal equipment, and adjacent abnormal equipment with corresponding distances less than a preset interval are grouped into the same density group, including: If the response determines that the required discharge volume for the same pipeline connection is greater than the actual discharge volume, the production equipment is identified as abnormal equipment, and the abnormal elements corresponding to each abnormal equipment and the factory area of ​​the corresponding production plant are determined based on the top view of the corresponding production plant. Establish an image coordinate system with the center point of the corresponding factory area as the origin, and divide the top view of the factory into different quadrant directions based on the image coordinate system to obtain the quadrant areas; In response to any anomalous element occupying at least two quadrant regions, determine the proportion of each element in each quadrant region that occupies the anomalous element, and determine that the anomalous element is located in the quadrant region with the largest proportion of the corresponding element. Using the center point of the region as the starting point of the line segment, generate the midpoint line of each quadrant region, and determine the direction of the line segment perpendicular to the midpoint line of the quadrant as the grouping direction; For all abnormal elements located in the same quadrant region, the spacing between corresponding adjacent elements along the grouping direction is determined. In response to any element spacing being less than a preset spacing, all abnormal devices corresponding to that element spacing are assigned to the same density group, resulting in density groups located in different quadrant regions.

7. The method according to claim 6, characterized in that, Each monitored site for concealed pipes located at the edge of a production plant, as determined by density zoning, is monitored. Based on the monitoring results, if any monitored site is found to contain a concealed sewage pipe, it is marked using a top-view diagram of the corresponding production plant, including: Based on the factory top view, the outline of the external equipment is generated to connect all abnormal equipment located in the same density division group. The first outline tangent line and the second outline tangent line are generated with the center point of the region as the starting point of the line segment. Determine the division region formed by the first contour tangent and the second contour tangent, and generate the division midline of the corresponding division region with the center point of the region as the starting point of the line segment; Obtain the number of anomalies for all abnormal devices in each density division group, and multiply the number of anomalies by a preset conversion coefficient to calculate the delay detection length; The control divider extends to a point outside the factory area and beyond the delay detection length, and generates a detection cutoff line that passes through the endpoint of the dividing divider and is perpendicular to the dividing divider. Control the first contour tangent and the second contour tangent to extend to connect with the detection cutoff line, determine the detection area outside the factory area formed by the first contour tangent and the second contour tangent, and determine the dark pipe monitoring plot at the edge of the production factory based on the detection area; Each site under surveillance for concealed pipes is inspected, and based on the inspection results, if it is determined that any site under surveillance for concealed pipes for sewage discharge exists, the site under surveillance for concealed pipes is marked based on the top view of the corresponding production plant.

8. The method according to claim 7, characterized in that, Each monitored site for concealed pipes is inspected, and based on the inspection results, if a sewage discharge pipe is determined to exist in any monitored site, the monitored site is marked based on the top view of the corresponding production plant, including: The dividing line is divided into arrays to obtain the array points of each division; Generate detection path lines for each division array point and extending to the first contour tangent and the second contour tangent, wherein each detection path line is perpendicular to the division midline. Each detection path is divided into arrays to obtain each detection path point. Based on the distance between the detection path points and the corresponding factory areas, each detection path is sorted from smallest to largest to obtain the first flight sequence. A first path direction parallel to the detection path line and a second path direction having an inverse relationship with the first path direction are determined. Based on the first flight sequence, all detection path points included in each detection path line corresponding to odd and even numbers are sorted according to the points of the first path direction and the second path direction to obtain the second flight sequence. A flight path is formed by combining the first flight sequence and the second flight sequence, and the UAV is controlled to fly along the flight path. When it arrives at each detection path point, the radar sensing unit pre-set on the UAV is controlled to perform a detection and obtain a detection data. The response determines the presence of a hidden sewage discharge pipe at any detection point based on a single detection data point, and marks the monitoring area of ​​the hidden pipe based on the factory top view of the corresponding production plant. Otherwise, it controls the drone to fly to the area common line of the corresponding factory area for secondary detection.

9. The method according to claim 8, characterized in that, Control the drone to fly to the public line in the corresponding factory area of ​​the inspection zone for secondary inspection, including: Determine the common line of the factory area corresponding to the detection area, and control the drone to fly to the center point of the corresponding common line segment; When the drone arrives at the center point of the line segment, the image acquisition unit set on the drone is controlled to acquire images and the edge images of the acquired area are identified. The response determines that there are dry areas in the region edge image based on the recognition results, determines that there are sewage discharge pipes in the region public line, and marks the region public line based on the factory top view of the corresponding production plant.

10. A smart water management full-process monitoring system, characterized in that, include: The emission acquisition module is configured to acquire the required emission amount of wastewater corresponding to each production equipment located in the production plant, and to acquire the actual emission amount of wastewater corresponding to each wastewater discharge pipeline located in the production plant that has pipeline connection with different production equipment based on the emission monitoring unit. The anomaly determination module is configured to determine that if the required emission amount for the same pipeline connection is greater than the actual emission amount, the production equipment is identified as an abnormal equipment, and adjacent abnormal equipment with a corresponding distance less than a preset distance are divided into the same density division group. The concealed pipe marking module detects each concealed pipe monitoring plot located at the edge of the production plant, which is determined based on density division groups, and responds by marking the concealed pipe monitoring plot based on the detection results to determine that any concealed pipe has a sewage discharge pipe.

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