A sewage treatment plant operation data real-time acquisition method and system

Abstract

This invention discloses a method and system for real-time acquisition of operational data from a wastewater treatment plant, belonging to the field of wastewater treatment technology. The method includes: initiating a monitoring cycle when the load information of the acquisition equipment meets preset load triggering conditions; within the monitoring cycle, acquiring operational data according to a key acquisition list to form a data set to be verified; detecting the data set to be verified, storing normal state data in a normal sampling data table, and replacing abnormal state data with normal sampling data from the previous time point before storing it in the normal sampling data table. This invention ensures the integrity and reliability of data acquisition through a load-based adaptive acquisition method and smoothing of abnormal data.

Description

Technical Field

[0001] This invention belongs to the field of wastewater treatment technology, specifically relating to a method and system for real-time acquisition of operational data from wastewater treatment plants. Background Technology

[0002] In wastewater treatment plants, the operational status of a series of complex equipment such as pumping stations, aeration systems, reaction tanks, and sedimentation tanks directly determines the efficiency of wastewater treatment, the quality of effluent, and the operating costs. Therefore, it is necessary to collect and analyze massive amounts of equipment operation data in real time and with intelligence to achieve precise monitoring of equipment status and early warning of faults.

[0003] In practical applications, existing status monitoring technologies for wastewater treatment equipment rely heavily on setting fixed thresholds or simple fluctuation ranges for single parameters. This makes it difficult to effectively distinguish between parameter fluctuations under normal operating conditions such as start-up, shutdown, and load changes, and true precursors to faults. This can easily lead to false alarms, causing alarm fatigue among maintenance personnel. Furthermore, frequent false alarms can reduce the reliability of the system, causing operators to ignore real fault signals.

[0004] To address the aforementioned issues, this invention provides a method and system for real-time acquisition of operational data from wastewater treatment plants. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for real-time acquisition of operational data of wastewater treatment plants, so as to solve the problem of misjudgment of dynamic parameters caused by changes in dynamic fluctuation values ​​in the prior art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for real-time acquisition of operational data from a wastewater treatment plant includes the following steps: When the load information of the data acquisition device is detected to meet the preset load triggering conditions, the monitoring cycle is started. The operational data collected during the monitoring period includes: Determine a key collection list containing multiple key collection time points, and collect runtime data based on the key collection list to form a data set to be verified; The dataset to be verified is inspected to identify whether there is any abnormal data in the dataset. The normal state data in the dataset to be verified is stored in the normal sampling data table. When abnormal state data is found, the abnormal state data is replaced with the normal sampling data from the previous time point and then stored in the normal sampling data table.

[0007] Preferably, the load information of the data acquisition device is detected to meet preset load triggering conditions, including: The system acquires the total running time of the acquisition device within the acquisition period and calculates the average load value as load information based on the total running time and the duration of the acquisition period. The average load value is then compared with the preset load limit to determine whether the preset load triggering condition is met.

[0008] Preferably, when the load information of the data acquisition device is detected to meet the preset load triggering conditions, the method further includes: If, within a series of consecutive acquisition cycles, the number of acquisition cycles in which the average load value meets the preset load triggering condition reaches a preset cycle number threshold, then the acquisition cycle will be updated to the preset optimized acquisition cycle.

[0009] Preferably, the detection of the data set to be verified includes: inputting the data set to be verified into a preset analysis model and running the preset analysis model to generate a data status result set that labels the data in the data set to be verified as normal or abnormal.

[0010] Preferably, after storing the normal state data from the dataset to be verified into the normal sampling data table, the method further includes: The data in the normal sampling data table are used as the reference baseline values; The subsequent sampled data that are later than the reference value in time are compared with the reference value to calculate the volatility of each subsequent sampled data. And based on the preset allowable fluctuation standard of the collection items corresponding to the volatility and subsequent sampling data, the subsequent sampling data is identified as steady state or wave dynamic.

[0011] Preferably, trend labeling is performed on subsequent sampled data identified as wave dynamics, specifically including: The difference between each subsequent sampled data identified as wave dynamic and the reference value is calculated to obtain a set of differences; And based on a statistical comparison of the number of positive differences and the number of negative differences in the difference set, the trend is marked as an upward trend or a downward trend.

[0012] Preferably, the method further includes a step of determining the predicted value at the next time step, specifically including: Based on the markers of upward or downward trends, a preset number of same-direction differences are selected from the difference set; The selected in-direction differences are averaged to obtain the prediction parameters; The predicted parameters are added to the last data point in time from the subsequent sampled data identified as wave dynamics to obtain the predicted value for the next time step.

[0013] This invention also discloses a real-time data acquisition system for wastewater treatment plant operations, comprising the following modules: The load assessment module is configured to acquire load information from the data acquisition device. The data acquisition strategy decision module is configured to ensure that the load information obtained by the response load assessment module meets the preset load triggering conditions, determine the monitoring period, and generate a key data acquisition list. The data acquisition execution module is configured to perform data acquisition based on the key acquisition list to form a set of data to be verified. The system also includes a data verification and processing module, configured to detect the dataset to be verified to identify whether there is abnormal data, store the normal data in the dataset to be verified into the normal sampling data table, and, when abnormal data is found, replace the abnormal data with the normal sampling data from the previous time point and store it into the normal sampling data table.

[0014] Preferably, the data verification processing module is configured as follows: The data in the normal sampling data table are used as the reference baseline values; The subsequent sampled data that are later than the reference value in time are compared with the reference value to calculate the volatility of each subsequent sampled data. And based on the preset allowable fluctuation standard of the collection items corresponding to the volatility and subsequent sampling data, the subsequent sampling data is identified as steady state or wave dynamic.

[0015] Preferably, the load assessment module is configured to: obtain the total running time of the acquisition device within the acquisition period, and calculate the average load value as load information based on the total running time and the duration of the acquisition period; compare the average load value with the preset load limit to determine whether the preset load triggering condition is met.

[0016] Beneficial effects 1. This invention collects operational data based on a key collection list during the monitoring period; it performs anomaly detection on the collected operational data and replaces the identified abnormal state data with normal sampling data from the previous time point to avoid data collection interruption caused by overload of the collection equipment, thereby ensuring the integrity and accuracy of the operational data. At the same time, it ensures the continuity of data collection through abnormal data smoothing processing to avoid data gaps interfering with subsequent analysis.

[0017] 2. This invention calculates the average load value and determines whether the number of consecutive collection cycles exceeding the preset load limit has reached a preset cycle number threshold, thereby dynamically adjusting the collection cycle. If the threshold is reached, the collection cycle is updated to a preset optimized collection cycle to adjust the collection frequency. Based on this adaptive collection strategy, the collection behavior can be optimized according to the load information of the collection equipment. While ensuring data validity, it avoids long-term overuse of the collection equipment, thereby reducing energy consumption and hardware wear and tear, and ensuring the long-term stability of the real-time data collection system for sewage treatment plant operation. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system module diagram of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely for explaining the invention and are not intended to limit the scope of protection of the invention.

[0020] Example 1 Please see Figure 1 As shown in the figure, this embodiment provides a method for real-time acquisition of operational data of a wastewater treatment plant, including the following steps: S1. Identify at least two key process units in the wastewater treatment plant that have a significant impact on the overall process flow and serve as monitoring targets. Based on the identified monitoring targets, construct a structured data table to be acquired. Key process units specifically include influent pump sets, aeration blowers, sludge return pumps, or chemical dosing pumps; The data table to be acquired serves as a data list, detailing the acquisition devices under each monitoring target and the acquisition items to be collected. It also records the communication protocol type and specific address information required to access each acquisition item, providing accurate addressing basis for subsequent automated data acquisition processes.

[0021] Furthermore, for the inlet pump, the data collected may include its operating current, outlet flow rate, and pump body temperature; for the aeration blower, the data collected may include air pressure, air volume, and motor speed.

[0022] S2. Based on the data table to be acquired, set an independent acquisition period for each acquisition item, request data from the acquisition device at each acquisition time point, and mark the acquired data as data to be verified. Based on the rate of change of the physical quantities reflected by the collected data and their importance level in process control, the data collection cycle is set to ensure data timeliness while rationally allocating system processing resources. Specifically: For influent flow rate data that changes rapidly, the collection period can be set to 1 minute; while for dissolved oxygen concentration in the aeration tank that changes relatively slowly, the collection period can be set to 5 minutes.

[0023] Based on the collection cycle set for each collection item, a series of specific actual collection time points are calculated and determined. At each actual collection time point, a data request is automatically initiated to the corresponding collection device to obtain its current operating data, and the raw operating data obtained this time is uniformly marked as data to be verified.

[0024] S3. At the end of each acquisition cycle, calculate the average load value of the acquisition device and compare it with the preset load limit. If the limit is exceeded, mark the current time point as a monitoring node. At the end of each acquisition cycle, load information is acquired to assess the processing load on the acquisition device's controller. The acquisition device's controller is either a programmable logic controller (PLC) or a station within a distributed control system.

[0025] Furthermore, the calculation steps for load information include: The total running time of the acquisition device within the recently concluded acquisition cycle is obtained, along with the standard duration of that acquisition cycle. The quantified average load value is obtained by calculating the ratio of the total running time to the duration of the acquisition cycle, and this average load is used as the load information.

[0026] Furthermore, the average load value is compared with the preset load limit for the device; if the calculated average load value is greater than or equal to the preset load limit, it indicates that the controller of the acquisition device is in a high-load operation state, and the current time point is immediately marked as a monitoring node.

[0027] The preset load limit is a safety threshold set based on the design specifications of the device controller and long-term stable operation experience.

[0028] S4. After calibrating the monitoring nodes, start the monitoring cycle and generate a key collection list containing multiple key collection time points within the monitoring cycle. Once a node is identified as a monitoring node at a certain point in time, an enhanced monitoring mode is activated, specifically: Based on the monitoring node and the preset monitoring duration, a specific time segment that starts from the monitoring node and lasts for a specified duration is determined, and this segment is defined as the monitoring period. During the monitoring period, the original regular data collection cycle is paused, and a higher frequency data collection strategy is implemented. This means that multiple key data collection time points with a denser distribution are identified within the monitoring period, and a key data collection list is generated. This allows for the acquisition of more dense data points during specific periods of high load on the device controller, which can then be used for subsequent refined analysis and status judgment.

[0029] Furthermore, the preset monitoring duration is preferably 30 minutes.

[0030] S5. During the monitoring period, collect data based on the key collection list to form a data set to be verified. Identify abnormal data through range verification and mutation verification. Store normal data in the normal sampling data table. Replace abnormal data with normal data from the previous time point and store it. Generate equipment inspection work orders based on the abnormality. During the monitoring period, based on the key collection list, operational data is collected at each key collection time point, and these data are aggregated to form a data set to be verified. Subsequently, the data set to be verified is subjected to data quality verification to identify whether there is any abnormal state data. This verification step verifies each piece of data in the data set to be verified one by one through a preset set of verification rules.

[0031] Furthermore, the preset set of verification rules includes at least: The range verification rules compare the data to be verified with the preset physical range of its corresponding acquisition item; wherein, the preset physical range includes the reasonable range of temperature and the upper and lower limits of pressure; The mutation verification rule compares the data to be verified with the normal sampled data at the previous time point to determine whether its rate of change exceeds the preset rate of change threshold.

[0032] Based on the verification results, each data point is labeled as either normal or abnormal, collectively forming a data status result set. Data labeled as normal in the data status result set is defined as normal status data and stored in the normal sampling data table.

[0033] If verification confirms the existence of abnormal data, to ensure the integrity and continuity of the data sequence, the abnormal data is replaced with normal sampling data recorded in the normal sampling data table that is immediately preceding it in time, forming a replacement data set. This replacement data is then stored in the normal sampling data table and marked as abnormal data with its source information. It will then be processed as a new data collection target, for example, automatically generating an equipment inspection work order to prompt maintenance personnel to check the relevant sensors or communication links.

[0034] S6. Select the reference benchmark value from the normal sampling data table, calculate the fluctuation of subsequent sampling data, compare it with the preset allowable fluctuation standard, and mark the data as a steady state or a wave dynamic. After accumulating a certain amount of data in the normal sampling data table, a stability analysis was performed on the newly entered data, as detailed below: Select a data entry from the normal sampling data table as a reference value. This reference value is usually the first data point entering the analysis sequence. For each subsequent sampled data point that is later than the reference baseline value in time, the absolute difference between the sampled data point and the reference baseline value is calculated in turn to obtain the volatility of each subsequent sampled data point. The calculated volatility is compared with the preset allowable volatility standard for the data collection item. If the volatility is less than the preset allowable volatility standard, the corresponding subsequent sampled data will be marked as being in a stable state; otherwise, if the volatility is greater than or equal to the preset allowable volatility standard, it will be marked as being in a volatile state.

[0035] The permissible fluctuation standard is a threshold value for a specific data item, used to determine whether the data is in a steady state or a wave dynamic.

[0036] S7. Calculate the set of differences between the wave dynamic data and the reference benchmark value, count the number of positive and negative differences to mark the upward or downward trend, and select the same-direction differences from the adjacent differences of the wave dynamic data to calculate the prediction parameters. For subsequent sampled data identified as being in wave dynamics, trend identification and prediction parameter calculation are further performed, specifically: The difference between each subsequent sampled data identified as being in wave dynamics and the reference value is calculated to obtain a set of differences; The signs of all differences in the difference set are counted to obtain the number of positive differences and the number of negative differences. That is, the total number of positive differences in the difference set is the number of positive differences, and the total number of negative differences in the difference set is the number of negative differences. By comparing the number of positive and negative differences, we can determine the overall direction of data change. If the number of positive differences is greater than or equal to the number of negative differences, the trend is marked as an upward trend; if the number of positive differences is less than the number of negative differences, the trend is marked as a downward trend. After determining the trend, in order to predict the data value at the next moment, the prediction parameters are calculated. The specific steps are as follows: Acquire all subsequent sampled data identified as being in a wave dynamic, and calculate the difference for each pair of temporally adjacent data to form a time series difference set; based on the marked upward or downward trend, select all differences in the same direction as the trend marking from this time series difference set, for example, if it is an upward trend, select all positive differences; from these same-direction differences, select a preset number of differences closest to the current time point according to the time order, calculate the arithmetic mean of the selected same-direction differences, and use the average value as the prediction parameter.

[0037] The same-direction difference is the difference in the time series difference set that is consistent with the determined upward or downward trend in both positive and negative directions.

[0038] S8. Add the predicted parameters to the last wave dynamic data to obtain the predicted value for the next moment, which is used for data filling. The predicted parameters calculated in the previous step are algebraically summed with the last data point in time among all subsequent sampled data identified as being in wave dynamics. The result is the predicted value for the next moment, which represents the next state value that the sampled item is most likely to reach under the currently identified wave trend.

[0039] Furthermore, when subsequent actual data collection fails for any reason or data verification is abnormal, the predicted value at the next moment can be used as an effective filler value, thereby ensuring the integrity of the data sequence without interrupting business analysis.

[0040] S9. Continuously monitor the number of load over-limit cycles within the continuous acquisition cycle. If the preset threshold is reached, update the acquisition cycle to the optimized acquisition cycle and output a prompt signal. The system adaptively adjusts the data collection cycle, continuously monitors and assesses the data.

[0041] Within multiple consecutive collection periods, determine whether the cumulative number of collection periods in which the average load value is greater than or equal to the preset load limit meets the preset period number threshold. If the condition is met, an alert signal will be output to warn the operator and simultaneously trigger the adjustment mechanism for the acquisition cycle. The currently executed acquisition cycle will be updated to a preset optimized acquisition cycle with a longer cycle duration, so as to proactively reduce the processing load on the acquisition device controller caused by the data acquisition task.

[0042] Furthermore, the average load value exceeded the limit for more than 3 out of 10 consecutive acquisition cycles, indicating that the relevant device controller was continuously under high processing pressure.

[0043] In addition, the preset optimized acquisition cycle will officially take effect at the beginning of the next acquisition cycle, thereby completing a closed-loop adaptive adjustment of the device load.

[0044] Example 2 See Figure 2 This embodiment provides a real-time data acquisition system for wastewater treatment plant operations, which can be logically divided into the following collaborative modules: The load assessment module is configured to continuously acquire and assess the load information of the data collection device to determine whether a more granular monitoring mode needs to be activated.

[0045] Specifically: The work is carried out in units of one acquisition cycle, and in each acquisition cycle, the total running time of the acquisition device for performing data reading and transmission tasks is recorded; Based on the total running time and the standard duration of the data collection cycle, the average load value is calculated and used as the load information for the current cycle. The calculated average load value is compared with the preset load limit. If the average load value is greater than or equal to the preset load limit, it is determined that the preset load triggering condition is met, and a trigger signal is sent to the data acquisition strategy decision module.

[0046] The data acquisition strategy decision module is activated after receiving a trigger signal from the load assessment module.

[0047] Specifically, a monitoring cycle is initiated. The duration of this monitoring cycle can differ from the data collection cycle and is usually set to address specific operating conditions. Identify a critical acquisition list containing multiple key acquisition time points. This list defines the specific moments during which data acquisition needs to be performed within the monitoring period. The acquisition frequency is typically higher than the frequency of the acquisition period to capture more process details that may occur during periods of high device load.

[0048] In addition, the module continuously counts the number of collection cycles that meet the preset load triggering conditions within multiple consecutive collection cycles. When the number reaches the preset number of cycles threshold, it determines that the system is in a high-load operation state for a long time and actively updates the collection cycle to the preset optimized collection cycle in order to more proactively adapt to and manage the continuous high system load.

[0049] The data acquisition and execution module is responsible for interacting with field equipment according to the strategy formulated by the acquisition strategy decision module. After receiving the key acquisition list, this module strictly sends data reading instructions to the target acquisition equipment according to the key acquisition time points specified on the list within the monitoring period. The operational data it collects includes various parameters of each process section of wastewater treatment, such as influent flow rate, dissolved oxygen concentration in the aeration tank, sludge interface height in the secondary sedimentation tank, and ammonia nitrogen content in the effluent. All data collected according to the key acquisition list within the monitoring period are aggregated to form a data set to be verified and then transmitted to the data verification and processing module.

[0050] The data verification and processing module is responsible for verifying the quality of the raw collected data and performing in-depth analysis. Its functions can be divided into the following consecutive stages: The module performs data quality verification and repair. After receiving the data set to be verified, it calls a preset analysis model to detect each data point in the set. The preset analysis model is preferably an anomaly detection algorithm based on historical data statistical features or a trained machine learning model. After the model runs, it generates a data status result set, in which each data point is marked as normal or abnormal. For all data marked as normal, the module directly stores it in a long-term stored normal sampling data table. For any abnormal data that is identified, such as values ​​exceeding the physical range or sudden spikes, the module performs a replacement operation: it queries and retrieves the normal sampling data from the previous time point from the normal sampling data table, uses this normal value to replace the current abnormal value, and then stores the replaced data in the normal sampling data table. This ensures the continuity and availability of the data table.

[0051] After the data is stored in the normal sampling data table, the module further performs stability analysis, selects a segment of data or a specific value from the normal sampling data table to determine the reference benchmark value; compares each subsequent sampling data that is later than the reference benchmark value in time with the reference benchmark value to calculate the volatility of each subsequent sampling data, which is preferably the absolute difference and the relative rate of change; based on the calculated volatility and the preset allowable volatility standard for the sampling item, each subsequent sampling data is identified as a steady state or a volatile state.

[0052] Trend marking is performed on the data identified as wave dynamics. The difference between each subsequent sampled data identified as wave dynamics and the previously determined reference value is calculated to obtain a set of differences. By counting and comparing the number of positive and negative differences in this set, the overall trend of data fluctuation is determined. For example, if the number of positive differences is significantly greater than the number of negative differences, the trend of the wave dynamic data segment is marked as an upward trend; otherwise, it is marked as a downward trend.

[0053] Based on the established trend, predict the next moment. Based on the established upward or downward trend, obtain all subsequent sampled data that are identified as being in a wave dynamic. Calculate the difference for each pair of temporally adjacent data to form a time series difference set. Select a preset number of positive differences from the time series difference set. For example, if it is an upward trend, select the three most recent positive differences. The selected in-direction differences are averaged to obtain the prediction parameter. By adding this prediction parameter to the last data in time of the subsequent sampled data identified as wave dynamics, the prediction value for the next time step can be obtained.

Claims

1. A method for real-time acquisition of operational data from a wastewater treatment plant, characterized in that, Includes the following steps: When the load information of the data acquisition device is detected to meet the preset load triggering conditions, the monitoring cycle is started. The operational data collected during the monitoring period includes: Determine a key collection list containing multiple key collection time points, and collect runtime data based on the key collection list to form a data set to be verified; The dataset to be verified is inspected to identify whether there is any abnormal data in the dataset. The normal state data in the dataset to be verified is stored in the normal sampling data table. When abnormal state data is found, the abnormal state data is replaced with the normal sampling data from the previous time point and then stored in the normal sampling data table.

2. The method for real-time acquisition of operational data of a wastewater treatment plant according to claim 1, characterized in that, The monitored load information of the data acquisition device meets the preset load trigger conditions, including: The system acquires the total running time of the acquisition device within the acquisition period and calculates the average load value as load information based on the total running time and the duration of the acquisition period. The average load value is then compared with the preset load limit to determine whether the preset load triggering condition is met.

3. The method for real-time acquisition of operational data of a wastewater treatment plant according to claim 2, characterized in that, The monitoring of the load information of the data acquisition device meets the preset load trigger conditions, including: If, within a series of consecutive acquisition cycles, the number of acquisition cycles in which the average load value meets the preset load triggering condition reaches a preset cycle number threshold, then the acquisition cycle will be updated to the preset optimized acquisition cycle.

4. The method for real-time acquisition of operational data of a wastewater treatment plant according to claim 1, characterized in that, The detection of the dataset to be verified includes: inputting the dataset to be verified into a preset analysis model and running the preset analysis model to generate a data status result set that labels the data in the dataset to be verified as normal or abnormal.

5. The method for real-time acquisition of operational data of a wastewater treatment plant according to claim 1, characterized in that, After storing the normal state data from the dataset to be verified into the normal sampling data table, the process also includes: The data in the normal sampling data table are used as the reference baseline values; The subsequent sampled data that are later than the reference value in time are compared with the reference value to calculate the volatility of each subsequent sampled data. And based on the preset allowable fluctuation standard of the collection items corresponding to the volatility and subsequent sampling data, the subsequent sampling data is identified as steady state or wave dynamic.

6. The method for real-time acquisition of operational data of a wastewater treatment plant according to claim 5, characterized in that, Trend labeling is performed on subsequent sampled data identified as wave dynamics, specifically including: The difference between each subsequent sampled data identified as wave dynamic and the reference value is calculated to obtain a set of differences; And based on a statistical comparison of the number of positive differences and the number of negative differences in the difference set, the trend is marked as an upward trend or a downward trend.

7. The method for real-time acquisition of operational data of a wastewater treatment plant according to claim 6, characterized in that, It also includes the step of determining the predicted value at the next time step, specifically including: Based on the markers of upward or downward trends, a preset number of same-direction differences are selected from the difference set; The selected in-direction differences are averaged to obtain the prediction parameters; The predicted parameters are added to the last data point in time from the subsequent sampled data identified as wave dynamics to obtain the predicted value for the next time step.

8. A real-time data acquisition system for wastewater treatment plant operations, used to execute the real-time data acquisition method for wastewater treatment plant operations as described in any one of claims 1-7, characterized in that, Includes the following modules: The load assessment module is configured to acquire load information from the data acquisition device. The data acquisition strategy decision module is configured to ensure that the load information obtained by the response load assessment module meets the preset load triggering conditions, determine the monitoring period, and generate a key data acquisition list. The data acquisition execution module is configured to perform data acquisition based on the key acquisition list to form a set of data to be verified. The system also includes a data verification and processing module, configured to detect the dataset to be verified to identify whether there is abnormal data, store the normal data in the dataset to be verified into the normal sampling data table, and, when abnormal data is found, replace the abnormal data with the normal sampling data from the previous time point and store it into the normal sampling data table.

9. A real-time data acquisition system for wastewater treatment plant operation according to claim 8, characterized in that, The data verification processing module is configured as follows: The data in the normal sampling data table are used as the reference baseline values; The subsequent sampled data that are later than the reference value in time are compared with the reference value to calculate the volatility of each subsequent sampled data. And based on the preset allowable fluctuation standard of the collection items corresponding to the volatility and subsequent sampling data, the subsequent sampling data is identified as steady state or wave dynamic.

10. A real-time data acquisition system for wastewater treatment plant operation according to claim 8, characterized in that, The load assessment module is configured as follows: The total running time of the data acquisition device within the acquisition cycle is obtained, and the average load value is calculated as the load information based on the total running time and the duration of the acquisition cycle. The average load value is compared with the preset load limit to determine whether the preset load triggering condition is met.

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