A data acquisition monitoring and analysis method

Abstract

This application provides a data acquisition, monitoring, and analysis method, relating to the field of data acquisition technology, to improve upon existing monitoring and analysis methods that fail to adequately consider the potential for excessively high cumulative discharge concentrations when wastewater from multiple wastewater treatment plants accumulates in the same water body. The method includes: monitoring water body information at the discharge outlets of each wastewater treatment plant, including the concentration and discharge volume of water quality indicators; analyzing the cumulative discharge volume of water quality indicators at each discharge outlet based on the water body information; analyzing the water body information of multiple discharge outlets to obtain the correlation degree between the water bodies from which wastewater is discharged; classifying discharge outlets with a correlation degree greater than a threshold as associated discharge outlets; obtaining the total discharge volume to the target water body based on the cumulative discharge volume of water quality indicators from multiple associated discharge outlets; and issuing a warning message when the total discharge volume reaches the load threshold of the target water body.

Description

Technical Field

[0001] This application relates to the field of data acquisition technology, and in particular to a data acquisition, monitoring and analysis method. Background Technology

[0002] In existing technologies, pollutant emission monitoring mainly focuses on the individual monitoring and analysis of pollutants discharged from each domestic wastewater treatment plant. This method fails to adequately consider the potential for excessively high cumulative emission concentrations when wastewater from multiple plants accumulates in the same body of water. When effluent from multiple wastewater treatment plants is discharged into the same water body, differences in treatment processes and pollutant removal efficiencies among the plants can lead to the total amount of pollutants in the receiving water body exceeding the environmental capacity, thereby causing water quality deterioration. Summary of the Invention

[0003] This application provides a data acquisition, monitoring, and analysis method to improve the problem that existing monitoring and analysis methods fail to adequately consider the potential for excessively high cumulative discharge concentrations when wastewater from multiple wastewater treatment plants accumulates in the same water body.

[0004] To achieve the above objectives, the embodiments of this application adopt the following technical solutions:

[0005] In a first aspect, embodiments of this application provide a data acquisition, monitoring, and analysis method, the method comprising: monitoring water body information at the discharge outlets of various wastewater treatment plants, the water body information including the concentration and discharge volume of water quality indicators; analyzing the water body information to obtain the cumulative discharge volume of the water quality indicators at each discharge outlet; analyzing the water area information of multiple discharge outlets to obtain the correlation degree of multiple discharge outlets, the correlation degree being used to indicate the degree of correlation between the water areas from which two discharge outlets discharge wastewater; classifying discharge outlets with a correlation degree greater than a threshold as associated discharge outlets; obtaining the total discharge volume to a target water area based on the cumulative discharge volume of water quality indicators from multiple associated discharge outlets; and issuing a warning message when the total discharge volume reaches the load threshold of the target water area.

[0006] In one possible implementation, the method further includes: obtaining the pre-purification concentration and post-purification concentration of the water quality indicator in the purification process of a wastewater treatment plant; and analyzing the pre-purification concentration and the post-purification concentration to obtain a purification identifier for the purification process, wherein the purification identifier is used to indicate the purification process's ability to purify the water quality indicator.

[0007] In one possible implementation, obtaining the purification label of the purification process based on the pre-purification concentration and the post-purification concentration includes: obtaining the purification label of the purification process based on the pre-purification concentration, the post-purification concentration, the standard concentration of the water quality indicator to be treated in the purification process, and a label calculation formula, wherein the label calculation formula is:

[0008] Where Q is the purification identifier for the purification process. This is the concentration before purification. This is the concentration after purification. That is the standard concentration.

[0009] In one possible implementation, the method further includes: obtaining influencing factors of water quality indicators; constructing a prediction model based on the influencing factors, the prediction model being used to predict the cumulative discharge of water quality indicators; obtaining historical discharge data and current actual discharge data of water quality indicators, the discharge data including discharge amounts for multiple different time periods; obtaining intermediate values ​​of the influencing factors based on the historical discharge data, current actual discharge data, and the prediction model; and predicting the cumulative discharge based on the prediction model corresponding to the intermediate values ​​of the influencing factors.

[0010] In one possible implementation, the influencing factors include a first influencing factor and a second influencing factor, wherein the first influencing factor is used to characterize the growth capacity of external water quality indicators, and the second influencing factor is used to characterize the purification capacity of the wastewater treatment plant.

[0011] In one possible implementation, the expression for the prediction model is:

[0012] in, Let m represent the cumulative discharge of water quality indicators at time t, and m represent the maximum load of water quality indicators that the target water area can carry. As the number one impact factor, It is the second most influential factor.

[0013] In one possible implementation, obtaining an intermediate value of the impact factor based on the historical emission data, the current actual emission data, and the prediction model includes: training a data acquisition model based on the historical emission data; acquiring first predicted emission data based on the data acquisition model; obtaining second predicted emission data based on the current actual emission data and the first predicted emission data; and obtaining an intermediate value of the impact factor based on the data of key nodes in the second predicted emission data and the prediction model.

[0014] In one possible implementation, obtaining the second predicted emission data based on the current actual emission data and the first predicted emission data includes: obtaining the average deviation between a first data point in the first predicted emission data and the current actual emission data, wherein the first data point is the data point in the first predicted emission data that corresponds to the current actual emission data; and correcting the first predicted emission data based on the average deviation to obtain the second predicted emission data.

[0015] In one possible implementation, the key nodes include: the time point of the first emission, the time point from the initial growth stage to the stable period, and the time point from the stable period to the long-term trend change.

[0016] Secondly, embodiments of this application also provide a data acquisition, monitoring, and analysis system, the system including a storage module and a processing module, the storage module being used to store computer instructions, and the processing module being used to execute the instructions to implement the method described in the first aspect.

[0017] In this way, by analyzing the correlation between multiple discharge outlets, this application defines discharge outlets with a correlation greater than a threshold as associated discharge outlets, monitors the cumulative discharge of associated discharge outlets, and issues a warning message when the cumulative discharge of water quality indicators from associated discharge outlets reaches the load threshold of the target water area. This can improve the problem in related technologies that have not fully considered the potential for excessively high cumulative discharge concentrations when wastewater from multiple wastewater treatment plants accumulates in the same water area. Attached Figure Description

[0018] Figure 1 A flowchart illustrating the methods provided for some embodiments of this application;

[0019] Figure 2 for Figure 1 A flowchart illustrating the process of the S600;

[0020] Figure 3 for Figure 2 A flowchart of the S630 process. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0022] In the following description, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0023] Furthermore, in this application, directional terms such as "upper," "lower," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and may change accordingly depending on the orientation of the components in the accompanying drawings.

[0024] In this application, unless otherwise expressly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral part; it can be a direct connection or an indirect connection through an intermediate medium. Furthermore, the term "electrical connection" can refer to the manner in which an electrical connection is used to achieve signal transmission.

[0025] As used herein, “about,” “approximately,” or “approximately” includes the stated value and a reference value within an acceptable range of deviation from the given value, wherein the acceptable range of deviation is determined by a person skilled in the art taking into account the measurement under discussion and the error associated with the measurement of the given quantity (i.e., the limitations of the measurement system).

[0026] In the embodiments of this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0027] In existing technologies, pollutant emission monitoring mainly focuses on the individual monitoring and analysis of pollutants discharged from each domestic wastewater treatment plant. This method fails to adequately consider the potential for excessively high cumulative emission concentrations when wastewater from multiple plants accumulates in the same body of water. When effluent from multiple wastewater treatment plants is discharged into the same water body, differences in treatment processes and pollutant removal efficiencies among the plants can lead to the total amount of pollutants in the receiving water body exceeding the environmental capacity, thereby causing water quality deterioration.

[0028] For example, studies have shown that long-term wastewater discharge has a significant effect on the accumulation of organic matter and nutrients in the sediment of receiving water bodies. The contents of organic matter (OM), total nitrogen (TN), and total phosphorus (TP) in the sediment near the discharge outlet are 1, 4, and 1 times higher than those in the control section, respectively. Furthermore, long-term wastewater discharge has a significant impact on enzyme activity in the sediment of receiving water bodies; the activities of dehydrogenase (DHA) and alkaline phosphatase (APA) in the sediment near the discharge outlet are approximately 3 times higher than those in the control section. These results indicate that the cumulative discharge from multiple wastewater treatment plants has a significant ecological effect on receiving water bodies, and existing technologies have failed to effectively monitor and manage this cumulative effect.

[0029] Therefore, this application provides a data acquisition, monitoring and analysis method to improve the problem that existing monitoring and analysis methods fail to adequately consider the potential for excessively high cumulative discharge concentrations when wastewater from multiple wastewater treatment plants accumulates in the same water body.

[0030] Before monitoring each domestic wastewater treatment plant, data from each plant needs to be synchronized to the monitoring and management system. In existing technologies, data acquisition is done point-to-point, connecting to the data acquisition systems of each wastewater treatment plant. Due to the large number of plants and the bulky size of the data acquisition equipment, the system platform needs to connect to each plant's data acquisition system individually. This method is inefficient and severely impacts the timeliness of data acquisition and the periodicity of system development and improvement. When data monitoring discrepancies or interruptions occur, each plant needs to be investigated and the problem resolved sequentially, resulting in weak data continuity and stability.

[0031] Therefore, the method of this application may include the following steps: providing the service IP address, port, and password of the monitoring and management system to each domestic wastewater treatment plant, and having the information management personnel of each plant configure the data acquisition equipment. The data acquisition equipment sends relevant monitoring data, including but not limited to water quantity and water quality, to the monitoring and management system. The monitoring and management system receives and parses the real-time data transmitted from the data acquisition equipment of each domestic wastewater treatment plant. After parsing, the data is compared with the standard values ​​to analyze the rationality of the monitoring data. Simultaneously, an early warning comparison system can be set to establish early warning thresholds for the monitoring data, thus improving the monitoring data anomaly mechanism.

[0032] The traditional approach, where the monitoring and management system collects data from individual wastewater treatment plant data acquisition devices, has been replaced by a system where each wastewater treatment plant configures its own service information and simultaneously collects wastewater treatment monitoring data into the monitoring and management system. This reduces the development and integration process, makes data access more flexible, and significantly reduces time costs associated with data collection and integration.

[0033] To address the issue that the aforementioned monitoring and analysis methods fail to adequately consider the potential for excessively high cumulative discharge concentrations when wastewater from multiple wastewater treatment plants accumulates in the same water body, such as... Figures 1 to 3 As shown, the method includes:

[0034] S100. Monitor the water body information at the discharge outlets of each wastewater treatment plant. The water body information includes the concentration of water quality indicators and the discharge volume. For example, data acquisition instruments at each wastewater treatment plant collect relevant water quality monitoring information, such as the discharge volume at the discharge outlet and the concentration of water quality indicators in the water body. These are not listed exhaustively in this application. Water quality indicators are used to indicate pollutants that need to be monitored and that pose a pollution hazard to the water body, including but not limited to chemical oxygen demand (COD), biochemical oxygen demand (BOD), ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), and heavy metals such as lead (Pb), mercury (Hg), cadmium (Cd), and chromium (Cr).

[0035] S200. The cumulative discharge of the water quality indicators at each of the discharge outlets is obtained based on the water body information analysis. For example, by monitoring water body information such as discharge volume and concentration, the discharge volume and cumulative discharge volume of the corresponding wastewater treatment plant's water quality indicators at each moment can be obtained. This is a common technical method used by those skilled in the art, and will not be elaborated upon here.

[0036] S300. The correlation degree of the multiple discharge outlets is obtained by analyzing the water area information of the multiple discharge outlets. The correlation degree is used to indicate the degree of correlation between the water areas from which two discharge outlets discharge sewage.

[0037] Wastewater treatment plants discharge treated wastewater into nearby water bodies; therefore, each discharge outlet corresponds to at least one discharge water body. Water body information can be used to represent the geographical location of the water body corresponding to the discharge outlet, including the water body's location and connectivity with other water bodies.

[0038] Among these, the highest degree of correlation is considered when two discharge outlets discharge into the same body of water, which can be defined as 100%. A relatively high degree of correlation is considered when the bodies of water discharged from the two outlets have a strong connection, such as a wide and deep connecting channel, which can be defined as 80%. A relatively weak degree of correlation is considered when the bodies of water discharged from the two outlets have a weak connection, which can be defined as 40%. The lowest degree of correlation is considered when the bodies of water discharged from the two outlets have no connection, which can be defined as 0%. Whether two bodies of water are connected can also be determined by those skilled in the art through surveying and mapping, and the degree of connectivity can be judged based on flow velocity measurement, channel width and depth, and flow rate calculation.

[0039] S400. Discharge outlets with a correlation degree greater than a threshold are classified as associated discharge outlets. It is understood that each discharge outlet may connect to multiple water bodies. When defining a discharge outlet as an associated discharge outlet based on its correlation degree, a central discharge outlet can be set. That is, the correlation degree between the central discharge outlet and other discharge outlets is defined based on the connectivity between the central discharge outlet's water body and the water bodies of other discharge outlets. Discharge outlets with a correlation degree higher than a threshold, such as 100%, are defined as associated discharge outlets. This application example will illustrate the case of multiple discharge outlets discharging into the same water body. Those skilled in the art can also set different thresholds based on the concept of this application.

[0040] S500: The total discharge to the target water area is obtained based on the cumulative discharge of water quality indicators from multiple associated discharge outlets. When the total discharge reaches the load threshold of the target water area, a warning message is issued.

[0041] For example, the total amount of water quality indicators that a target water area can handle is limited, and this total amount can be defined as a load threshold. This value can be assessed by someone skilled in the art, such as through measurement or calculation based on water area information. When the cumulative discharge of water quality indicators from all associated discharge outlets reaches the load threshold of the target water area, a warning message can be issued to notify management personnel.

[0042] In this way, by analyzing the correlation between multiple discharge outlets, this application defines discharge outlets with a correlation greater than a threshold as associated discharge outlets, monitors the cumulative discharge of associated discharge outlets, and issues a warning message when the cumulative discharge of water quality indicators from associated discharge outlets reaches the load threshold of the target water area. This can improve the problem in related technologies that have not fully considered the potential for excessively high cumulative discharge concentrations when wastewater from multiple wastewater treatment plants accumulates in the same water area.

[0043] In some embodiments, the method further includes:

[0044] S100. Obtain the concentrations of water quality indicators before and after purification in the purification process of the wastewater treatment plant.

[0045] S200. Based on the concentration before purification and the concentration after purification, a purification identifier for the purification process is obtained. The purification identifier is used to indicate the ability of the purification process to purify water quality indicators.

[0046] Wastewater treatment plants involve multiple purification processes. To better assess the purification capacity of a wastewater treatment plant, such as when the cumulative discharge from a related outlet is too high, the purification capacity of each purification process in the wastewater treatment plant corresponding to that outlet can be analyzed.

[0047] For example, the concentrations of water quality indicators before and after purification in each purification process of the wastewater treatment plant are obtained through data acquisition and monitoring equipment such as data acquisition instruments in each wastewater treatment plant.

[0048] The purification label for the purification process is obtained based on the pre-purification concentration, the post-purification concentration, the standard concentration of the water quality indicators treated in the purification process, and the label calculation formula. The label calculation formula is as follows:

[0049] Where Q is the purification identifier for the purification process. This is the concentration before purification. This is the concentration after purification. It refers to the standard discharge concentration of water quality indicators.

[0050] In this way, the purification capacity of each purification process in each wastewater treatment plant can be obtained to determine the water quality indicators.

[0051] In the above embodiments, although the cumulative emissions from the associated emission outlets have been monitored, it is only a monitoring and analysis of data events that have already occurred. It lacks the ability to predict future pollution situations and makes it difficult to take preventative measures against future pollution events.

[0052] In some embodiments, the method further includes:

[0053] S300. Obtain the influencing factors of water quality indicators. For example, the influencing factors include a first influencing factor and a second influencing factor, whereby the first influencing factor characterizes the growth capacity of external water quality indicators, and the second influencing factor characterizes the purification capacity of the wastewater treatment plant.

[0054] The first influencing factor can represent the increase in emissions due to external pollution sources (such as industrial emissions, agricultural pollution, etc.). This refers to a sudden increase in pollutant emissions from these external sources at a specific point in time or within a specific period. For example, if the establishment of an industrial zone leads to a significant increase in the discharge of water quality indicators, data from that point in time can be used to estimate the first influencing factor. The second influencing factor can represent the effect of improved wastewater treatment plant capacity on curbing the increase in emissions, such as a reduction in emissions resulting from process improvements at wastewater treatment plants.

[0055] S400. A prediction model is constructed based on the aforementioned influencing factors. This prediction model is used to predict the cumulative discharge of water quality indicators. For example, the expression of the prediction model is:

[0056] in, Let m represent the cumulative discharge of water quality indicators at time t, and m represent the maximum load of water quality indicators that the target water area can carry. As the number one impact factor, It is the second most influential factor.

[0057] S500: Obtain historical discharge data and current actual discharge data of water quality indicators, wherein the discharge data includes discharge amounts for multiple different time periods.

[0058] Historical and current actual discharge data of water quality indicators are obtained through data acquisition equipment at various wastewater treatment plants and monitoring equipment in the target water area. For example, data before 2024 are historical discharge data, discharge data from January to August 2024 are current actual discharge data, and data from September to December 2024 are data to be predicted.

[0059] S600. Based on the historical emission data, current actual emission data, and the prediction model, obtain intermediate values ​​for the impact factor. For example, obtaining intermediate values ​​for the impact factor based on the historical emission data, current actual emission data, and the prediction model includes:

[0060] S610. Train a data acquisition model based on the historical emission data.

[0061] For example, the intermediate model can be composed of multiple prediction models. For instance, the intermediate prediction model can be composed of a weighted average of adaptive probabilistic neural network models, fuzzy neural network models, and BP neural network models. The weight of each model can be determined by the accuracy of its prediction results; the higher the accuracy, the greater its weight in constructing the intermediate prediction model.

[0062] S620. Obtain the first predicted emission data according to the data acquisition model.

[0063] The trained data acquisition model is used to predict the cumulative discharge of water quality indicators in the water area, and the first predicted discharge data is obtained, such as the data from September 2024 to December 2024.

[0064] S630. Obtain second predicted emission data based on the current actual emission data and the first predicted emission data. Exemplarily, obtaining the second predicted emission data based on the current actual emission data and the first predicted emission data includes:

[0065] S631. Obtain the average deviation between the first data in the first predicted emission data and the current actual emission data, wherein the first data is the data in the first predicted emission data that corresponds to the current actual emission data.

[0066] The data corresponding to the current actual emission data in the first predicted emission data are extracted, that is, the data from January 2024 to August 2024 in the first predicted emission data are analyzed with the actual emission data from January 2024 to August 2024 in the current actual emission data, and the average deviation between the two is obtained.

[0067] For example, to calculate the monthly deviation between the first predicted emissions data and the actual emissions data from January 2024 to August 2024, the average deviation for that period is obtained by summing all the deviation values ​​and dividing by the number of months.

[0068] S632. Correct the first predicted emission data according to the average deviation to obtain the second predicted emission data. Predict the cumulative emissions according to the prediction model corresponding to the intermediate value of the influencing factor.

[0069] For example, the first predicted emissions data is corrected by adding an average deviation to the predicted data for each month in the first predicted emissions data, resulting in the second predicted emissions data. Understandably, the second predicted emissions data includes data from January 2024 to December 2024, that is, it includes both data that has occurred and data that has not yet occurred / is yet to be predicted.

[0070] S640. Based on the data of key nodes in the second predicted emission data and the prediction model, obtain the intermediate value of the influence factor.

[0071] For example, a certain water pollutant was first discharged in January 2023, with an initial discharge of 2 tons. In the following months, the discharge rate steadily increased: 4 tons from February to March 2023, 5 tons from April to August 2023, 3 tons from September to November 2023, and 2 tons from December 2023 to June 2024. It can be seen that January 2023 was the initial discharge date. April 2023 marked the transition from the initial growth phase to a stable period. September 2023 represented the point where the stable period transitioned to a long-term trend change. Therefore, several key points exist throughout the entire lifecycle of water pollution: the initial discharge date, the transition from the initial growth phase to a stable period, and the point where the stable period transitions to a long-term trend change.

[0072] By obtaining the emission data corresponding to each key node in the second predicted emission data, substituting them into the prediction model, and solving the prediction model, the intermediate values ​​of the corresponding impact factors can be obtained.

[0073] S700. Predict the cumulative emissions based on the prediction model corresponding to the intermediate value of the influencing factor.

[0074] The intermediate values ​​of the obtained impact factors are substituted into the prediction model, that is, the determined first and second impact factors are substituted into the prediction model, and the data to be predicted is predicted according to the prediction model. That is, the cumulative emissions from September 2024 to December 2024 are predicted using the model.

[0075] Secondly, embodiments of this application also provide a data acquisition, monitoring, and analysis system, the system including a storage module and a processing module, the storage module being used to store computer instructions, and the processing module being used to execute the instructions to implement the method described in the foregoing embodiments.

[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods in the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0077] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0078] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0079] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0080] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data acquisition, monitoring, and analysis method, characterized in that, The method includes: The system monitors the water quality information at the discharge outlets of each wastewater treatment plant and the purification capacity of each wastewater treatment plant's purification process. The water quality information includes the concentration of water quality indicators and the discharge volume. The purification capacity is determined based on the concentration of water quality indicators before and after purification in the purification process. The cumulative discharge of the water quality indicators at each discharge outlet is obtained based on the water body information analysis. The correlation degree of multiple discharge outlets is obtained by analyzing the water area information of multiple discharge outlets. The correlation degree is used to indicate the degree of correlation between the water areas from which two discharge outlets discharge sewage. The water area information includes the location information of the water area and its connectivity information with other water areas. The emission outlets with a correlation degree greater than the threshold are classified as associated emission outlets; The total discharge to the target water area is obtained based on the cumulative discharge of water quality indicators from multiple associated discharge outlets. When the total discharge reaches the load threshold of the target water area, a warning message is issued. The method further includes: Obtain the influencing factors of water quality indicators, wherein the influencing factors include at least the purification capacity of the wastewater treatment plant; A prediction model is constructed based on the aforementioned influencing factors, and the prediction model is used to predict the cumulative discharge of water quality indicators. Obtain historical and current actual discharge data of water quality indicators, including discharge amounts for multiple different time periods; Based on the historical emission data, current actual emission data, and the prediction model, intermediate values ​​of the influencing factors are obtained. The cumulative emissions are predicted based on the prediction model corresponding to the intermediate value of the influencing factor; The influencing factors include a first influencing factor and a second influencing factor. The first influencing factor is used to characterize the growth capacity of external water quality indicators, and the second influencing factor is used to characterize the purification capacity of the wastewater treatment plant. The expression for the prediction model is as follows: ,in, Let m represent the cumulative discharge of water quality indicators at time t, and m represent the maximum load of water quality indicators that the target water area can carry. As the number one impact factor, It is the second most influential factor.

2. The data acquisition, monitoring, and analysis method according to claim 1, characterized in that, The method further includes: Obtain the concentrations of water quality indicators before and after purification in the purification process of a wastewater treatment plant; The purification identifier of the purification process is obtained by analyzing the concentration before purification and the concentration after purification. The purification identifier is used to indicate the ability of the purification process to purify water quality indicators.

3. The data acquisition, monitoring, and analysis method according to claim 2, characterized in that, The step of obtaining the purification identifier for the purification process based on the pre-purification concentration and the post-purification concentration includes: The purification label for the purification process is obtained based on the pre-purification concentration, the post-purification concentration, the standard concentration of the water quality indicators treated in the purification process, and the label calculation formula. The label calculation formula is as follows: Where Q is the purification identifier for the purification process. This is the concentration before purification. This is the concentration after purification. That is the standard concentration.

4. The data acquisition, monitoring, and analysis method according to claim 1, characterized in that, Based on the historical emission data, current actual emission data, and the prediction model, intermediate values ​​of the influencing factors are obtained, including: The model is trained based on the historical emission data. The first predicted emission data is obtained based on the data acquisition model; The second predicted emission data is obtained based on the current actual emission data and the first predicted emission data; Based on the data of key nodes in the second predicted emissions data and the prediction model, the intermediate values ​​of the influencing factors are obtained.

5. The data acquisition, monitoring, and analysis method according to claim 4, characterized in that, The step of obtaining the second predicted emission data based on the current actual emission data and the first predicted emission data includes: The average deviation between a first data point in the first predicted emission data and the current actual emission data is obtained, wherein the first data point is the data in the first predicted emission data that corresponds to the current actual emission data. The first predicted emission data is corrected based on the average deviation to obtain the second predicted emission data.

6. The data acquisition, monitoring, and analysis method according to claim 4, characterized in that, The key milestones include: the time of first emission, the time from the initial growth phase to the stable phase, and the time from the stable phase to the long-term trend change.

7. A data acquisition, monitoring, and analysis system, characterized in that, The system includes a storage module and a processing module. The storage module is used to store computer instructions, and the processing module is used to execute the instructions to implement the method as described in any one of claims 1 to 6.

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