High-efficiency data acquisition monitoring analysis method

Abstract

The invention provides a high-efficiency data acquisition monitoring analysis method, relates to the technical field of data acquisition, and is used for improving the problem that in related technologies, an existing monitoring analysis method cannot fully consider that the accumulated discharge concentration is too high when sewage discharged by multiple sewage plants is accumulated to the same water area. The method comprises the following steps: monitoring water body information of a discharge outlet of each sewage treatment plant, wherein the water body information comprises the concentration and displacement of a water quality index; analyzing according to the water body information to obtain the accumulated discharge amount of the water quality index of each discharge port; analyzing according to the water area information of the plurality of discharge outlets to obtain the correlation degree of the plurality of discharge outlets, wherein the correlation degree is used for indicating the correlation degree between the water areas of the sewage discharged by the two discharge outlets; the discharge outlets with the correlation degree larger than a threshold value are divided into correlated discharge outlets; the total discharge amount discharged to the target water area is obtained according to the accumulated discharge amount of the water quality indexes of the multiple associated discharge outlets, and warning information is sent out when the total discharge amount reaches the load threshold value of the target water area.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data acquisition technology, and in particular to an efficient data acquisition, monitoring and analysis method. Background Art

[0002] In the existing technology, the monitoring of pollutant emissions mainly focuses on the individual monitoring and analysis of pollutants discharged by each domestic sewage treatment plant. This method fails to fully consider the problem of excessive cumulative emission concentrations that may be caused when sewage discharged from multiple sewage treatment plants accumulates in the same water area. When the tail water of multiple sewage treatment plants is discharged into the same water body, due to the differences in treatment processes and pollutant removal efficiency of each plant, the total amount of pollutants in the receiving water body will exceed the environmental capacity, causing water quality deterioration. Summary of the invention

[0003] The embodiment of the present application provides a highly efficient data collection, monitoring and analysis method to improve the problem of excessive cumulative discharge concentration that may be caused when sewage discharged from multiple sewage treatment plants accumulates in the same water area in existing monitoring and analysis methods.

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

[0005] In a first aspect, an embodiment of the present application provides an efficient data collection, monitoring and analysis method, the method comprising: monitoring water body information of each discharge outlet of a sewage treatment plant, the water body information including the concentration and discharge volume of water quality indicators; obtaining the cumulative discharge of the water quality indicators of each discharge outlet based on the water body information; obtaining the correlation of the multiple discharge outlets based on the water area information of the multiple discharge outlets, the correlation being used to indicate the degree of correlation between the water areas from which two discharge outlets discharge sewage; dividing the discharge outlets whose correlation is greater than a threshold into associated discharge outlets; obtaining the total discharge to the target water area based on the cumulative discharge of water quality indicators of the multiple associated discharge outlets, and issuing a warning message when the total discharge reaches the load threshold of the target water area.

[0006] In a possible implementation, the method further includes: obtaining the pre-purification concentration and the post-purification concentration of the water quality indicator in the purification process of the sewage treatment plant; and obtaining a purification mark of the purification process based on the pre-purification concentration and the post-purification concentration, wherein the purification mark is used to indicate the ability of the purification process to purify the water quality indicator.

[0007] In a possible implementation, the obtaining of the purification identification of the purification process according to the pre-purification concentration and the post-purification concentration analysis includes: obtaining the purification identification of the purification process according to the pre-purification concentration, the post-purification concentration, the standard concentration of the water quality indicator treated by the purification process, and an identification calculation formula, wherein the identification calculation formula is:

[0008]

[0009] Among them, Q is the purification mark of the purification process, Cin is the concentration before purification, Cout is the concentration after purification, and Cstd is the standard concentration.

[0010] In a possible implementation, the method also includes: obtaining an influencing factor of a water quality indicator; constructing a prediction model based on the influencing factor, the prediction model being used to predict the cumulative emission of the water quality indicator; obtaining historical emission data and current actual emission data of the water quality indicator, the emission data including emissions in multiple different time periods; obtaining an intermediate value of the influencing factor based on the historical emission data, the current actual emission data and the prediction model; and predicting the cumulative emission based on the prediction model corresponding to the intermediate value of the influencing factor.

[0011] In a possible implementation, the influencing factor includes a first influencing factor and a second influencing factor, the first influencing factor is used to characterize the growth capacity of the external water quality indicator, and the second influencing factor is used to characterize the purification capacity of the sewage treatment plant.

[0012] In a possible implementation, the prediction model is expressed as:

[0013]

[0014] Among them, N(t) is the cumulative discharge of water quality indicators at time t, m represents the maximum load of water quality indicators that the target water area can carry, p is the first influencing factor, and q is the second influencing factor.

[0015] In one possible implementation, an intermediate value of the influencing factor is obtained based on the historical emission data, the current actual emission data and the prediction model, including: 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 influencing factor based on data of key nodes in the second predicted emission data and the prediction model.

[0016] In one possible implementation, the second predicted emission data is obtained based on the current actual emission data and the first predicted emission data, including: obtaining the average deviation between first data in the first predicted emission data and the current actual emission data, the first data being the data in the first predicted emission data corresponding to the current actual emission data; and correcting the first predicted emission data based on the average deviation to obtain the second emission prediction data.

[0017] In a possible implementation, the key nodes include: the time point of first emission, the time point from the initial growth period to the stable period, and the node of trend change from the stable period to the long-term period.

[0018] In a second aspect, an embodiment of the present application further provides a high-efficiency data acquisition, monitoring and analysis system, the system comprising a storage module and a processing module, the storage module being used to store computer instructions, the processing module being used to execute the instructions to implement the method described in the first aspect.

[0019] In this way, the present application analyzes the correlation between multiple discharge outlets, defines the discharge outlets with a correlation greater than a threshold as associated discharge outlets, monitors the cumulative discharge of the associated discharge outlets, and issues a warning message when the cumulative discharge of water quality indicators at the associated discharge outlets reaches the load threshold of the target water area. This can improve the problem of excessive cumulative discharge concentration that may be caused by the accumulation of sewage discharged from multiple sewage treatment plants in the same water area in the relevant technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of a method provided for some embodiments of the present application;

[0021] Figure 2 for Figure 1 Schematic diagram of the process flow of S600;

[0022] Figure 3 for Figure 2 Schematic diagram of the process flow of S630. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0024] In the following, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0025] In addition, in the present application, directional terms such as "up", "down", "left" and "right" may be defined including but not limited to the orientation relative to the schematic placement of the components in the drawings. It should be understood that these directional terms may be relative concepts, which are used for relative description and clarification, and may change accordingly according to the change in the orientation of the components in the drawings.

[0026] In this application, unless otherwise specified or limited, the term "connection" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. In addition, the term "electrical connection" can be a way of achieving electrical connection for signal transmission.

[0027] As used herein, “about,” “substantially,” or “approximately” includes the stated value and reference values ​​that are within an acceptable range of deviation from the particular value, where the acceptable range of deviation is as determined by one of ordinary skill in the art taking into account the measurements in question and the errors associated with the measurement of the particular quantity (i.e., the limitations of the measurement system).

[0028] In the embodiments of the present 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 the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a specific way.

[0029] In the existing technology, the monitoring of pollutant emissions mainly focuses on the individual monitoring and analysis of pollutants discharged by each domestic sewage treatment plant. This method fails to fully consider the problem of excessive cumulative emission concentrations that may be caused when sewage discharged from multiple sewage treatment plants accumulates in the same water area. When the tail water of multiple sewage treatment plants is discharged into the same water body, due to the differences in treatment processes and pollutant removal efficiency of each plant, the total amount of pollutants in the receiving water body will exceed the environmental capacity, causing water quality deterioration.

[0030] For example, studies have shown that long-term sewage discharge has a significant effect on the accumulation of organic matter and nutrients in the sediment of the receiving water body. The content of organic matter (OM), total nitrogen (TN), and total phosphorus (TP) in the sediment near the discharge port is 1 times, 4 times, and 1 times higher than that of the control section, respectively. In addition, long-term sewage discharge has a significant effect on the enzyme activity in the sediment of the receiving water body. The dehydrogenase activity (DHA) and alkaline phosphatase activity (APA) in the sediment near the discharge port are about 3 times higher than that of the control section. These research results show that the cumulative discharge of multiple sewage treatment plants has a significant ecological effect on the receiving water body, and existing technologies have failed to effectively monitor and manage this cumulative effect.

[0031] Therefore, the embodiment of the present application provides a highly efficient data collection, monitoring and analysis method to improve the existing monitoring and analysis method which fails to fully consider the problem of excessive cumulative discharge concentration that may be caused when sewage discharged from multiple sewage treatment plants accumulates in the same water area.

[0032] Before monitoring each domestic sewage treatment plant, the data of each domestic sewage treatment plant needs to be synchronized to the monitoring and management system. In the existing technology, data collection is done in a point-to-point manner by connecting to the data acquisition system of the domestic sewage treatment plant. Due to the large number of plants and the large volume of data acquisition equipment, the system platform needs to connect to the data acquisition system of each domestic sewage treatment plant separately for each plant system platform. This access method is inefficient and seriously affects the timeliness of data collection and connection and the periodicity of system construction and improvement. When there are differences or interruptions in data monitoring, it is also necessary to check and solve the problems in each plant in turn, and the continuous stability of data is not strong.

[0033] Therefore, the method of the present application may include the following steps: providing the service IP address, port and password of the monitoring and management system to each domestic sewage treatment plant, and the information management personnel of each plant complete the configuration service information of the data acquisition instrument equipment. The data acquisition instrument 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 the real-time data transmitted by the data acquisition instruments of each domestic sewage treatment plant and parses it. After the analysis is completed, it is compared with the standard value of the data to analyze the rationality of the monitoring data. At the same time, an early warning comparison system can also be set up to set the early warning threshold of the monitoring data, so that the abnormal mechanism of monitoring data is more perfect.

[0034] The traditional monitoring and management system has changed from collecting data from data acquisition instruments at each domestic sewage treatment plant to configuring service information at each domestic sewage treatment plant, and synchronously collecting domestic sewage treatment monitoring information data to the monitoring and management system. This reduces the R&D docking process, makes data access more flexible, and greatly reduces the time cost of docking during the data collection process.

[0035] In order to solve the problem that the above monitoring and analysis methods fail to fully consider the problem that the accumulated discharge concentration may be too high when the sewage discharged from multiple sewage treatment plants accumulates in the same water area. Figures 1 to 3 As shown, the method includes:

[0036] S100. Monitor the water body information of the discharge outlet of each sewage treatment plant, the water body information including the concentration and discharge volume of water quality indicators. Exemplarily, the discharge volume of each sewage treatment plant discharge outlet and the concentration of water quality indicators in the water body and other related water quality monitoring information are collected by the data acquisition instrument of each sewage treatment plant, which are not listed one by one in this application. Among them, water quality indicators are used to indicate pollutants that need to be monitored and are harmful to water bodies, including but not limited to chemical oxygen demand (COD), biochemical oxygen demand (BOD), ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), heavy metals: such as lead (Pb), mercury (Hg), cadmium (Cd), chromium (Cr), etc.

[0037] S200, obtaining the cumulative discharge of the water quality indicators of each of the discharge ports according to the water body information analysis. Exemplarily, by monitoring water body information such as discharge volume and concentration, the discharge volume and cumulative discharge volume of the water quality indicators discharged by the corresponding sewage treatment plant at each moment can be obtained. This is a conventional technical means used by those skilled in the art, and this application will not elaborate on it here.

[0038] S300. Analyze the water area information of the plurality of discharge outlets to obtain the correlation degree of the plurality of discharge outlets, wherein the correlation degree is used to indicate the correlation degree between the water areas where sewage is discharged from two discharge outlets.

[0039] The sewage treatment plant discharges the treated sewage into the water area near the sewage treatment plant. Therefore, each discharge port corresponds to at least one discharge water area. The water area information can be used to indicate the geographical location information of the water area corresponding to the discharge port, including the location information of the water area, the connectivity information with other water areas, etc.

[0040] Among them, when the two discharge outlets discharge into the same water area, it is considered that the degree of association is the highest, such as the degree of association can be defined as 100%. When the water areas discharged by the two discharge outlets have a strong connection relationship, such as there is a wider and deeper connecting channel between them, it can be considered that the corresponding two discharge outlets have a high degree of association, such as the degree of association can be defined as 80%. When the water areas discharged by the two discharge outlets have a weaker connection relationship, it can be considered that the corresponding two discharge outlets have a weaker connection relationship, such as the degree of association can be defined as 40%. When the water areas discharged by the two discharge outlets do not have a connection relationship, it can be considered that the corresponding two discharge outlets have the lowest degree of association, such as the degree of association can be defined as 0%. Whether the two water areas are connected can also be obtained by mapping by those skilled in the art, and the degree of connectivity between the two water areas can be judged based on flow velocity measurement, channel width and depth, flow calculation, etc.

[0041] S400, dividing the discharge outlets whose correlation is greater than a threshold into associated discharge outlets. It is understandable that the water area of ​​each discharge outlet may be connected to multiple water areas. When the discharge outlet is defined as an associated discharge outlet based on the correlation, a central discharge outlet may be set, that is, the correlation between the water area of ​​the central discharge outlet and the water areas of other discharge outlets is defined based on the connectivity between the water area of ​​the central discharge outlet and the water areas of other discharge outlets, and the discharge outlets with a correlation higher than a threshold such as 100% are defined as associated discharge outlets. The example of this application will be explained with the case where multiple discharge outlets discharge into the same water area, and those skilled in the art may also set different thresholds based on the concept of this application.

[0042] S500, obtaining a total discharge amount to a target water area according to the cumulative discharge amounts of water quality indicators of the plurality of associated discharge outlets, and issuing a warning message when the total discharge amount reaches a load threshold of the target water area.

[0043] For example, the total amount of water quality indicators that the target water area can carry is limited, and the total amount that it can carry can be defined as a load threshold. The evaluation of this value can be set by a person skilled in the art, such as measurement or calculation based on information of the water area. When the cumulative total amount of water quality indicators discharged from each associated discharge port reaches the load threshold of the target water area, a warning message can be issued to notify the management personnel.

[0044] In this way, the present application analyzes the correlation between multiple discharge outlets, defines the discharge outlets with a correlation greater than a threshold as associated discharge outlets, monitors the cumulative discharge of the associated discharge outlets, and issues a warning message when the cumulative discharge of water quality indicators at the associated discharge outlets reaches the load threshold of the target water area. This can improve the problem of excessive cumulative discharge concentration that may be caused by the accumulation of sewage discharged from multiple sewage treatment plants in the same water area in the relevant technology.

[0045] In some embodiments, the method further comprises:

[0046] S100, obtaining the concentration before purification and the concentration after purification of the water quality index in the purification process of the sewage treatment plant.

[0047] S200. Analyze and obtain a purification mark of the purification process according to the pre-purification concentration and the post-purification concentration, wherein the purification mark is used to indicate the ability of the purification process to purify water quality indicators.

[0048] The sewage treatment plant contains multiple purification processes. In order to better evaluate the purification capacity of the sewage treatment plant, if the cumulative discharge of the associated drainage outlet is too high, the purification capacity of each purification process of the sewage treatment plant corresponding to the associated drainage outlet can be analyzed.

[0049] Exemplarily, the pre-purification concentration and post-purification concentration of the water quality indicators in each purification process of the sewage treatment plant are obtained through data acquisition instruments and other data collection and monitoring equipment of each sewage treatment plant.

[0050] The purification mark of the purification process is obtained according to the concentration before purification, the concentration after purification, the standard concentration of the water quality index treated by the purification process, and the mark calculation formula, and the mark calculation formula is:

[0051]

[0052] Among them, Q is the purification mark of the purification process, Cin is the concentration before purification, Cout is the concentration after purification, and Cstd is the standard discharge concentration of the water quality indicator.

[0053] In this way, the ability of each purification process in each sewage purification plant to purify water quality indicators can be obtained.

[0054] In the above embodiment, although the cumulative emissions of the associated emission outlets have been monitored, it is only the monitoring and analysis of data events that have occurred, and there is a lack of the ability to predict future pollution situations, making it difficult to take precautions against future pollution events in advance.

[0055] In some embodiments, the method further comprises:

[0056] S300, obtaining the influencing factors of the water quality indicators. Exemplarily, 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 the external water quality indicators, and the second influencing factor is used to characterize the purification capacity of the sewage treatment plant.

[0057] The first impact factor can represent the increase in emissions due to external pollution sources (such as industrial emissions, agricultural pollution, etc.), and the sudden increase in pollutant emissions due to these external pollution sources at a specific time point or time period. For example, if the establishment of an industrial zone leads to a significant increase in the discharge of water quality indicators, the data at this time point can be used to estimate the first impact factor. The second impact factor can represent the improvement of the purification capacity of the sewage treatment plant to curb the growth of emissions, such as the reduction of emissions caused by the sewage treatment plant after the process improvement.

[0058] S400: construct a prediction model based on the influencing factors, wherein the prediction model is used to predict the cumulative discharge of water quality indicators. For example, the prediction model is expressed as:

[0059]

[0060] Among them, N(t) is the cumulative discharge of water quality indicators at time t, m represents the maximum load of water quality indicators that the target water area can carry, p is the first influencing factor, and q is the second influencing factor.

[0061] S500: Acquire historical emission data and current actual emission data of water quality indicators, wherein the emission data includes emission amounts in multiple different time periods.

[0062] The historical emission data and current actual emission data of water quality indicators are obtained through the data acquisition equipment of each sewage treatment plant and the monitoring equipment of the target water area. For example, the data before 2024 are historical emission data, the emission data from January 2024 to August 2024 are current actual emission data, and the data from September 2024 to December 2024 are data to be predicted.

[0063] S600: Obtaining an intermediate value of the influencing factor according to the historical emission data, the current actual emission data, and the prediction model. Exemplarily, obtaining an intermediate value of the influencing factor according to the historical emission data, the current actual emission data, and the prediction model includes:

[0064] S610: Training a data acquisition model according to the historical emission data.

[0065] Exemplarily, the intermediate model may be composed of multiple prediction models. For example, the intermediate prediction model may be composed of a weighted adaptive probabilistic neural network model, a fuzzy neural network model, and a BP neural network model. The weight of each model may be determined by the accuracy of its prediction result. The higher the accuracy, the greater its weight when forming the intermediate prediction model.

[0066] S620. Acquire first predicted emission data according to the data acquisition model.

[0067] The trained data acquisition model is used to predict the cumulative emissions of water quality indicators in the water area to obtain the first predicted emission data, such as predicting the data from September 2024 to December 2024.

[0068] S630: Obtain second predicted emission data according to current actual emission data and the first predicted emission data. Exemplarily, obtaining the second predicted emission data according to current actual emission data and the first predicted emission data includes:

[0069] S631. Obtain an average deviation between first data in the first predicted emission data and the current actual emission data, where the first data is data in the first predicted emission data corresponding to the current actual emission data.

[0070] The data corresponding to the current actual emission data in the first predicted emission data is taken out, that is, the data from January 2024 to August 2024 in the first predicted emission data, and the actual emission data from January 2024 to August 2024 in the current actual emission data are analyzed to obtain the average deviation between the two.

[0071] For example, the monthly deviation between the first predicted emission data and the actual emission data from January 2024 to August 2024 is calculated, the sum of all deviation values ​​is divided by the number of months to obtain the average deviation for that period of time.

[0072] S632: Correct the first predicted emission data according to the average deviation to obtain the second predicted emission data. Predict the cumulative emission according to the prediction model corresponding to the intermediate value of the influencing factor.

[0073] Exemplarily, the forecast data of each month in the first forecast emission data is added with the average deviation to correct the first forecast emission data to obtain the second forecast emission data. It can be understood that the second forecast emission data includes data from January 2024 to December 2024, that is, data that has occurred and data that has not occurred / to be predicted.

[0074] S640: Obtain an intermediate value of the influencing factor based on the data of the key nodes in the second predicted emission data and the prediction model.

[0075] For example, a certain water pollutant is first discharged in January 2023, with an emission of 2 tons in the first month. In the following months, the increase in emissions continues to rise, with an increase of 4 tons from February 2023 to March 2023, and an increase of 5 tons from April 2023 to August 2023. From September 2023 to November 2023, the increase in emissions is 3 tons. From December 2023 to June 2024, the increase in emissions is 2 tons. It can be seen that January 2023 is the time point of the first emission. April 2023 is the time point from the initial growth period to the stable period. September 2023 is the node of change from the stable period to the long-term trend. Therefore, in the entire life cycle of water pollution, there can be several key nodes: the time point of the first emission, the time point from the initial growth period to the stable period, and the node of change from the stable period to the long-term trend.

[0076] The corresponding emission data of each key node in the second predicted emission data is obtained, substituted into the prediction model, and the prediction model is solved to obtain the intermediate value of the corresponding influencing factor.

[0077] S700. Predict the cumulative emissions according to the prediction model corresponding to the intermediate value of the influencing factor.

[0078] Substitute the obtained intermediate value of the influencing factor into the prediction model, that is, substitute the determined first influencing factor and the second influencing factor into the prediction model, and predict the data to be predicted according to the prediction model, that is, use the model to predict the cumulative emissions from September 2024 to December 2024.

[0079] In a second aspect, an embodiment of the present application further provides a high-efficiency data acquisition, monitoring and analysis system, the system comprising a storage module and a processing module, the storage module being used to store computer instructions, the processing module being used to execute the instructions to implement the method described in the aforementioned embodiment.

[0080] Through the description of the above implementation methods, technicians in the relevant field can clearly understand that the methods in the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course, 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 relevant technology, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0081] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned 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.

[0082] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0083] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0084] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated units may be implemented in the form of hardware.

[0085] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application shall be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A highly efficient data collection, monitoring and analysis method, characterized in that: The method comprises: Monitoring water body information at the discharge outlets of each sewage treatment plant, the water body information including the concentration of water quality indicators and discharge volume; Obtaining the cumulative discharge amount of the water quality indicator from each of the discharge outlets according to the water body information analysis; Analyzing the water area information of the plurality of discharge outlets to obtain the correlation degree of the plurality of discharge outlets, the correlation degree is used to indicate the correlation degree between the water areas where sewage is discharged from two discharge outlets; dividing the discharge outlets whose correlation degree is greater than a threshold into associated discharge outlets; The total discharge amount discharged to the target water area is obtained according to the cumulative discharge amount of the water quality indicators of the plurality of associated discharge outlets, and a warning message is issued when the total discharge amount reaches the load threshold of the target water area.

2. The highly efficient data acquisition, monitoring and analysis method according to claim 1 is characterized in that: The method further comprises: Obtaining the pre-purification concentration and post-purification concentration of the water quality index in the purification process of the sewage treatment plant; The purification mark of the purification process is obtained according to the analysis of the concentration before purification and the concentration after purification, and the purification mark is used to indicate the ability of the purification process to purify water quality indicators.

3. The high-efficiency data acquisition, monitoring and analysis method according to claim 2 is characterized in that: The step of obtaining a purification mark of the purification process according to the pre-purification concentration and the post-purification concentration analysis includes: The purification mark of the purification process is obtained according to the concentration before purification, the concentration after purification, the standard concentration of the water quality index treated by the purification process, and the mark calculation formula, and the mark calculation formula is: Among them, Q is the purification mark of the purification process, Cin is the concentration before purification, Cout is the concentration after purification, and Cstd is the standard concentration.

4. The highly efficient data acquisition, monitoring and analysis method according to claim 1 is characterized in that: The method further comprises: Obtain the influencing factors of water quality indicators; Constructing a prediction model based on the influencing factors, wherein the prediction model is used to predict the cumulative discharge of water quality indicators; Acquire historical discharge data and current actual discharge data of water quality indicators, wherein the discharge data includes discharge amounts in multiple different time periods; Obtaining an intermediate value of the influencing factor according to the historical emission data, the current actual emission data and the prediction model; The cumulative emissions are predicted according to the prediction model corresponding to the intermediate value of the influencing factor.

5. The highly efficient data acquisition, monitoring and analysis method according to claim 4 is characterized in that: 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 an external water quality indicator, and the second influencing factor is used to characterize the purification capacity of a sewage treatment plant.

6. The high-efficiency data acquisition, monitoring and analysis method according to claim 5 is characterized in that: The expression of the prediction model is: Among them, N(t) is the cumulative discharge of water quality indicators at time t, m represents the maximum load of water quality indicators that the target water area can carry, p is the first influencing factor, and q is the second influencing factor.

7. The high-efficiency data acquisition, monitoring and analysis method according to claim 6 is characterized in that: According to the historical emission data, the current actual emission data and the prediction model, an intermediate value of the influencing factor is obtained, including: training a data acquisition model based on the historical emissions data; Acquire first predicted emission data according to the data acquisition model; Obtaining second predicted emission data according to current actual emission data and the first predicted emission data; An intermediate value of the influencing factor is obtained based on the data of the key nodes in the second predicted emission data and the prediction model.

8. The high-efficiency data acquisition, monitoring and analysis method according to claim 7 is characterized in that: The obtaining second predicted emission data according to the current actual emission data and the first predicted emission data includes: Obtaining an average deviation between first data in the first predicted emission data and the current actual emission data, the first data being data in the first predicted emission data corresponding to the current actual emission data; The first predicted emission data is corrected according to the average deviation to obtain the second predicted emission data.

9. The high-efficiency data acquisition, monitoring and analysis method according to claim 7, characterized in that: The key nodes include: the time point of first emission, the time point from the initial growth period to the stable period, and the time point of change from the stable period to the long-term trend.

10. A high-efficiency data acquisition, monitoring and analysis system, characterized in that: The system comprises a storage module and a processing module, wherein the storage module is used to store computer instructions, and the processing module is used to execute the instructions to implement the method according to any one of claims 1 to 9.

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