Sewage treatment flow online monitoring system

Abstract

The application discloses a sewage treatment flow online monitoring system, comprising: a window sliding unit, a sequence positioning unit, a strength calculation unit, an output load determination unit, which is used for dynamically determining a delayed mutation drainage interval of a sewage flow mutation sequence on a time axis based on comprehensive fluctuation strength and an interval output load of the mutation drainage interval; an input load determination unit, which is used for acquiring K delayed input sewage flows of the mutation drainage interval and calculating an interval input load based on the K delayed input sewage flows; and a load state judgment unit, which is used for judging a load state according to the interval output load and the interval input load and performing scheduling based on the load state; the application monitors actual turnover processes of sewage mutation flows in treatment equipment, accurately monitors a state that a treatment system still needs to continuously run after mutation ends, triggers multi-stage scheduling accordingly, and realizes response linkage of sewage treatment equipment.

Description

Technical Field

[0001] This invention relates to wastewater treatment monitoring systems, specifically an online wastewater treatment flow monitoring system. Background Technology

[0002] In wastewater treatment systems, the influent flow rate often fluctuates abruptly due to factors such as concentrated industrial emissions, extreme weather, and sudden diversions in municipal sewer systems. This sudden increase in flow rate within a short period can significantly impact treatment equipment. Traditional flow monitoring and scheduling methods often focus on the instantaneous response to the sudden change, neglecting a crucial reality: wastewater experiences a physical processing delay within the treatment equipment; the sudden flow is not discharged immediately but rather released with a lag. This means that even after the sudden flow ends, the treatment system must continue operating under pressure for a period of time.

[0003] Patent document CN118113968A discloses an online monitoring system for wastewater treatment flow, which helps to assess the performance and pollution status of wastewater treatment systems. However, existing technologies generally lack monitoring of wastewater flow during the "delayed release" phase, leading to assessment bias in the monitoring system. This can easily underestimate the continuous load pressure caused by sudden changes, ultimately resulting in untimely scheduling response or strategy imbalance, affecting the treatment efficiency of wastewater treatment equipment. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an online wastewater treatment flow monitoring system that solves the technical problems mentioned in the background by introducing flow monitoring during a delayed release phase.

[0005] To achieve the above objectives, the present invention provides the following technical solution: An online monitoring system for wastewater treatment flow, the system comprising: A sliding window unit is used to define a sliding future time window on the timeline; Sequence localization unit, used to locate abrupt changes in wastewater flow within a future time window; The intensity calculation unit is used to calculate the overall fluctuation intensity of the wastewater flow mutation sequence; The output load determination unit is used to dynamically determine the delayed abrupt drainage interval of the sewage flow mutation sequence on the time axis based on the comprehensive fluctuation intensity, as well as the interval output load of the abrupt drainage interval. The input load determination unit is used to obtain K delayed input sewage flow rates in the sudden drainage interval and calculate the interval input load based on the K delayed input sewage flow rates. The load status determination unit is used to determine the load status based on the interval output load and interval input load, and to perform scheduling based on the load status.

[0006] In some specific embodiments, a sliding future time window is defined on the timeline, including: S1-1, Mark the current timestamp on the timeline; S1-2. Starting from the current timestamp, advance the preset standard duration along the time axis to determine a target timestamp for monitoring sewage flow. S1-3. Define a future time window of fixed length between the current timestamp and the target timestamp; wherein the future time window covers N timestamps and is always aligned with the target timestamp; In some specific embodiments, locating abrupt changes in wastewater flow within a future time window includes: S2-1. Using a pre-trained ARIMA model, obtain N predicted sewage flow rates within a future time window; S2-2. Based on the predicted N wastewater flow rates, mark the start and end timestamps of the abrupt change on the time axis. S2-3. Locate the sewage flow mutation sequence based on the start mutation timestamp and the end mutation timestamp.

[0007] In some specific embodiments, based on N predicted wastewater flow rates, the start and end timestamps of the abrupt change are marked on the time axis, including: S2-2-1. Using a single timestamp as the step size, slide the future time window successively and update the corresponding N predicted sewage flows. S2-2-2. Extract the predicted sewage flow rate of the target timestamp and its adjacent timestamps from the N predicted sewage flow rates after each future time window slide. S2-2-3. Based on the predicted sewage flow rate of the target timestamp and its adjacent timestamps, calculate the instantaneous flow rate difference formed by each future time window sliding. S2-2-4. If the instantaneous flow difference is greater than the first threshold, then the target timestamp corresponding to this sliding is marked as the starting change timestamp; wherein, the first threshold is used to identify the positive change threshold for predicting the increase of sewage flow. S2-2-5. Continue executing steps S2-2-1 to S2-2-3. If the instantaneous flow difference is less than the second threshold, then mark the target timestamp corresponding to this sliding as the end of the mutation timestamp. The second threshold is used to identify the negative change threshold for predicting the decline in sewage flow.

[0008] In some specific embodiments, the wastewater flow mutation sequence is located based on the start mutation timestamp and the end mutation timestamp, including: S2-3-1. Anchor M consecutive mutation timestamps between the start mutation timestamp and the end mutation timestamp; S2-3-2. From N predicted wastewater flow rates, select M abrupt wastewater flow rates corresponding to M consecutive timestamps of abrupt changes. S2-3-3. Arrange the M abruptly changed wastewater flow rates in time stamp order to construct an abruptly changed flow rate sequence; In some specific embodiments, the comprehensive fluctuation intensity between the start and end timestamps of a wastewater flow mutation is calculated based on the wastewater flow mutation sequence, including: S3-1. In the M abrupt changes in wastewater flow rate in the wastewater flow rate mutation sequence, calculate the M-1 flow rate differences between adjacent abrupt changes in wastewater flow rate. S3-2. Normalize the M-1 flow differences to obtain the standard flow difference set; S3-3. Calculate the variance of the standard flow difference set, and define the variance as the fluctuation intensity of the sewage flow mutation sequence; S3-4. Calculate the total flow rate of the sudden changes based on the M sudden changes in wastewater flow rate; S3-5. Calculate the average flow intensity based on the total flow of the mutation and the duration of the flow mutation between the start and end timestamps. S3-6. The average flow intensity and the fluctuation intensity are weighted and summed according to the set weights to obtain the comprehensive fluctuation intensity.

[0009] In some specific embodiments, based on the comprehensive fluctuation intensity, the delayed abrupt discharge interval of the sewage flow mutation sequence and the interval output load of the abrupt discharge interval are dynamically determined on the time axis, including: S4-1. Based on the comprehensive fluctuation intensity, the total flow rate of the sudden change, and the predefined sewage treatment efficiency, calculate the sewage treatment time for the M sudden sewage flows to circulate within the sewage treatment equipment. S4-2. Based on the sewage treatment time of M sudden sewage flow rates circulating in the sewage treatment equipment and the predefined minimum standard delay, determine the sudden discharge interval of the M sudden sewage flow rates through the sewage treatment equipment. S4-3. Calculate the output load of the interval based on the sudden change drainage interval and its corresponding sudden change total flow.

[0010] In some specific embodiments, the interval input load corresponding to the abrupt drainage interval is calculated based on K delayed input sewage flow rates, including: S5-1. Use a pre-trained ARIMA model to obtain the K delayed input sewage flow rates for the abrupt drainage interval; S5-2. Calculate the total sewage flow rate of the sudden drainage interval based on the K delayed input sewage flow rates of the sudden drainage interval; S5-3. Calculate the input load of the drainage interval based on the total sewage flow rate of the interval with sudden changes; In some specific embodiments, the load status is determined based on the interval output load and the interval input load, and scheduling is performed based on the load status, including: S6-1. Calculate the combined load of the abrupt drainage interval based on the interval output load and interval input load; S6-2. If the total load exceeds the set threshold, the wastewater treatment equipment is determined to be in an overload state. S6-3. If the sewage treatment equipment is determined to be in an overload state, calculate the response time between the current timestamp and the starting point of the sudden drainage interval, and perform hierarchical scheduling based on the response time.

[0011] This invention provides an online monitoring system for wastewater treatment flow, which has the following advantages: By monitoring the actual turnover process of abruptly changing wastewater flow within the treatment equipment, this invention introduces the concept of abruptly changing discharge intervals. This concretizes the delayed discharge process of the abruptly changing flow after it enters the treatment equipment, thereby accurately monitoring the state where the treatment system needs to continue operating after the abrupt change ends. Furthermore, by introducing delayed input wastewater flow, this invention continuously calculates the influent intensity per unit time within the abruptly changing discharge interval, characterizing the actual load pressure borne by the wastewater treatment equipment during the delayed phase, filling the gap in traditional assessments that ignore the treatment delay. Furthermore, the input load and discharge load during the delayed phase are combined to form a comprehensive load index, which triggers multi-level scheduling to achieve responsive linkage of the wastewater treatment equipment. Attached Figure Description

[0012] Figure 1 This is a structural block diagram of an online wastewater treatment flow monitoring system according to the present invention; Figure 2 This is a schematic diagram of the monitoring process of an online wastewater treatment flow monitoring system according to the present invention; Figure 3 This is a schematic diagram of the process for determining the comprehensive fluctuation intensity described in this invention; Figure 4 This is a schematic diagram of the execution process of the hierarchical scheduling described in this invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Example 1: Please refer to Figures 1 to 2 This invention provides an online monitoring system for wastewater treatment flow, the system comprising: A sliding window unit is used to define a sliding future time window on the timeline; Sequence localization unit, used to locate abrupt changes in wastewater flow within a future time window; The intensity calculation unit is used to calculate the overall fluctuation intensity of the wastewater flow mutation sequence; The output load determination unit is used to dynamically determine the delayed abrupt drainage interval of the sewage flow mutation sequence on the time axis based on the comprehensive fluctuation intensity, as well as the interval output load of the abrupt drainage interval. The input load determination unit is used to obtain K delayed input sewage flow rates in the sudden drainage interval and calculate the interval input load based on the K delayed input sewage flow rates. The load status determination unit is used to determine the load status based on the interval output load and interval input load, and to perform scheduling based on the load status.

[0015] This invention constructs a sliding future time window through a window sliding unit, identifies abrupt change intervals based on a sequence positioning unit, and calculates the corresponding fluctuation intensity based on an intensity calculation unit. On this basis, an output load determination unit and an input load determination unit are introduced to process the delay, mapping the abrupt flow to a delayed drainage interval, and obtaining the output load and delayed input load of the interval respectively. Finally, the load status is determined based on the load status determination unit and scheduling is executed, realizing the monitoring and response of the sewage treatment system under dynamic flow disturbance.

[0016] Example 2: See Figures 3 to 4 The technical solution that differs from that of Embodiment 1 in that Embodiment 2 discloses the application steps of each unit in Embodiment 1.

[0017] For example, the application steps of the window sliding unit include: S1-1, Mark the current timestamp on the timeline; S1-2. Starting from the current timestamp, advance the preset standard duration along the time axis to determine a target timestamp for monitoring sewage flow. Specifically, the preset standard duration represents a fixed monitoring span from the current timestamp to future predictions; it can be set according to the equipment processing cycle of the wastewater treatment system.

[0018] S1-3. Define a future time window of fixed length between the current timestamp and the target timestamp; wherein the future time window covers N timestamps and is always aligned with the target timestamp; Specifically, the alignment of the future time window with the target timestamp means that no matter how the future time window slides, its end will always coincide with the currently predicted target timestamp.

[0019] In this embodiment, by constructing a future time window that slides synchronously with the target timestamp, the fixed time range on which the sewage flow prediction depends is defined, and the time window mechanism ensures that each prediction operation has a consistent time span.

[0020] For example, the application steps of the sequence positioning unit include: S2-1. Using a pre-trained ARIMA model, obtain N predicted sewage flow rates within a future time window; For example, the ARIMA model (Autoregressive Integral Moving Average model) analyzes historical sewage flow time series data, extracts autoregressive, differencing, and moving average features, and thus constructs a dynamic model that can describe and predict flow change trends.

[0021] In this embodiment, the training of the ARIMA model includes the following steps: (1) Collect time series data of influent flow rate of the sewage treatment system within a set time period in the past; (2) Perform stationarity analysis on the original influent flow time series data. If the data shows a trend or unstable fluctuation, perform difference processing to transform it into a stationary series. (3) Based on the autocorrelation function (ACF) and partial autocorrelation function (PACF) curves, identify the lag characteristics and dependency structure of the time series, and then select the model order parameters to construct the ARIMA model; (4) The maximum likelihood estimation method is used to train the model parameters to establish the model's predictive ability for historical flow behavior. In this specification, the ARIMA model is used to predict sewage flow within a future target time window.

[0022] S2-2. Based on the predicted N wastewater flow rates, mark the start and end timestamps of the abrupt change on the time axis. S2-3. Locate the sewage flow mutation sequence based on the start mutation timestamp and the end mutation timestamp.

[0023] In this embodiment, the wastewater flow rate within a future time window is predicted based on the ARIMA model, and the start and end timestamps of the mutation are identified. The location interval of potential mutation behavior on the time axis is effectively extracted, and a wastewater flow mutation sequence with temporal continuity and mutation characteristics is constructed, thus forming a sequence structure that can be analyzed.

[0024] Further, step S2-2 specifically includes: S2-2-1. Using a single timestamp as the step size, slide the future time window successively and update the corresponding N predicted sewage flows. Specifically, after each slide, only the predicted sewage flow at the latest timestamp is the newly added predicted value, while the predicted sewage flow corresponding to the earliest timestamp in the future time window is removed from the window.

[0025] S2-2-2. Extract the predicted sewage flow rate of the target timestamp and its adjacent timestamps from the N predicted sewage flow rates after each future time window slide. S2-2-3. Based on the predicted sewage flow rate of the target timestamp and its adjacent timestamps, calculate the instantaneous flow rate difference formed by each future time window sliding. S2-2-4. If the instantaneous flow difference is greater than the first threshold, then the target timestamp corresponding to this sliding is marked as the starting change timestamp; wherein, the first threshold is used to identify the positive change threshold for predicting the increase of sewage flow. S2-2-5. Continue executing steps S2-2-1 to S2-2-3. If the instantaneous flow difference is less than the second threshold, then mark the target timestamp corresponding to this sliding as the end of the mutation timestamp. The second threshold is used to identify the negative change threshold for predicting the decline in sewage flow.

[0026] In this embodiment, by constructing a sliding prediction mechanism with a single timestamp as the step size, and calculating the instantaneous flow difference based on the target timestamp and its neighboring values ​​in each slide, continuous monitoring and real-time response to sudden changes in sewage flow trends can be achieved. Combined with a dual-threshold determination strategy for both positive and negative changes, the start and end boundaries of the sudden change process can be effectively defined, thereby dynamically extracting the sudden change segment formed by the flow surge and decline processes from the continuous prediction sequence.

[0027] Furthermore, steps S2-3 specifically include: S2-3-1. Anchor M consecutive mutation timestamps between the start mutation timestamp and the end mutation timestamp; S2-3-2. From N predicted wastewater flow rates, select M abrupt wastewater flow rates corresponding to M consecutive timestamps of abrupt changes. S2-3-3. Arrange the M abruptly changed wastewater flow rates in time stamp order to construct an abruptly changed flow rate sequence; It should be noted that the time intervals corresponding to the aforementioned abrupt flow sequences are usually not triggered by a single event, but rather by the combined effects of multiple external factors on the wastewater system, resulting in periodic or sudden discharge behaviors. Examples include concentrated industrial emissions during holidays, backflow into municipal sewer systems due to heavy rainfall, or load shifts in regional drainage systems. In these scenarios, the abnormal increase in wastewater flow is often continuous rather than an instantaneous peak, and the flow may still exhibit localized irregular fluctuations during this period. Therefore, although the overall abrupt sequence is under high load, frequent flow fluctuations still exist within it. This combination of "high total volume + high volatility" significantly increases the requirements for dynamic adjustment in wastewater treatment systems.

[0028] In this embodiment, by anchoring consecutive time points between the start and end mutation timestamps and constructing a time-ordered mutation flow sequence, the sequence representation provides a structured basis for load measurement and dynamic response.

[0029] For example, the application steps of the intensity calculation unit include: S3-1. In the M abrupt changes in wastewater flow rate in the wastewater flow rate mutation sequence, calculate the M-1 flow rate differences between adjacent abrupt changes in wastewater flow rate. Specifically, the abrupt sewage flow in the sewage flow mutation sequence comes from predictions corresponding to consecutive timestamps, and therefore possesses a natural sequential nature. Since mutations are often accompanied by multi-source emission disturbances and pipeline response lags, the abrupt sewage flow between adjacent timestamps usually does not remain absolutely stable, and will inevitably exhibit a certain degree of short-term fluctuation, that is, there will inevitably be a flow difference.

[0030] S3-2. Normalize the M-1 flow differences to obtain the standard flow difference set; S3-3. Calculate the variance of the standard flow difference set, and define the variance as the fluctuation intensity of the sewage flow mutation sequence; The formula for calculating the wave intensity is: ; in, This represents the flow difference between the i-th abruptly changed wastewater flow rate and the adjacent abruptly changed wastewater flow rates in the wastewater flow rate mutation sequence. This represents the average of M-1 flow differences, where M represents the number of sudden changes in wastewater flow. This represents the variance of the standard flow difference set.

[0031] Specifically, the fluctuation intensity is obtained by calculating the variance of the difference in abrupt wastewater flow between adjacent time stamps. It is used to measure the severity of the abrupt change in wastewater flow over a short period of time, from the initial time stamp to the end time stamp. During wastewater treatment, although wastewater treatment equipment has a certain load buffering capacity, when the flow rate frequently experiences large jumps within a short period, the equipment needs to continuously adjust its operating status to cope with the fluctuations. This may lead to increased energy consumption, decreased operating efficiency, and even affect the stability of the effluent quality.

[0032] therefore: If the fluctuation intensity is large, it indicates that the flow rate fluctuates drastically and changes unpredictably during that period, requiring frequent responses. If the fluctuation intensity is small, even if the average load is high, it indicates that the load change is stable and the system is more likely to maintain continuous processing.

[0033] Furthermore, the reason for constructing a time-sequential sequence of abrupt changes in flow is that the key to dealing with fluctuations lies in the "continuous jumps between adjacent moments," rather than the overall discreteness of the flow set. While conventional statistical indicators such as the coefficient of variation and skewness can measure the global distribution, they cannot reflect the magnitude of flow jumps per unit time. Therefore, fluctuation intensity constructed based on the difference between adjacent flow rates is a necessary indicator for identifying high-frequency disturbances.

[0034] S3-4. Calculate the total flow rate of the sudden changes based on the M sudden changes in wastewater flow rate; S3-5. Calculate the average flow intensity based on the total flow of the mutation and the duration of the flow mutation between the start and end timestamps. S3-5. The average flow intensity and the fluctuation intensity are weighted and summed according to the set weights to obtain the comprehensive fluctuation intensity.

[0035] Specifically, average flow intensity measures the overall load level, while fluctuation intensity measures the severity of short-term disturbances. Both reflect the regulatory pressure faced by the wastewater treatment system during abrupt changes. The weights for both are empirically set weighting coefficients, ensuring a weight sum of 1, and can be dynamically configured based on the system's regulatory capacity and steady-state preferences.

[0036] In this embodiment, by constructing a sequence of adjacent differences in abrupt flow and calculating its variance, not only is the total load level within the abrupt range captured, but also the intensity of flow disturbance on a short time scale. Finally, a weighted combination of average intensity and fluctuation intensity is used as a comprehensive index to characterize the control complexity faced by the wastewater system within this abrupt range.

[0037] For example, the application steps of the output load determination unit include: S4-1. Based on the comprehensive fluctuation intensity, the total flow rate of the sudden change, and the predefined sewage treatment efficiency, calculate the sewage treatment time for the M sudden sewage flows to circulate within the sewage treatment equipment. Specifically, the wastewater treatment efficiency represents the volume of wastewater that can be treated per unit time. Therefore, the ratio of the total flow rate to the wastewater treatment efficiency constitutes the ideal treatment time (i.e., the standard time required when the flow rate is stable and the fluctuation is extremely low). In this embodiment, the comprehensive fluctuation intensity is used to characterize the severity of local fluctuations between adjacent timestamps in the flow rate sequence. The higher the value, the more frequently the equipment needs to adjust its operating status, resulting in a decrease in treatment efficiency.

[0038] Therefore, the formula for calculating the wastewater treatment time is: ; in, Wastewater treatment time (unit: h) represents the total time required to treat sudden flow changes; The total wastewater flow rate (unit: m³) is within the abrupt change interval. Wastewater treatment efficiency (unit: m³ / h) represents the volume of wastewater that a wastewater treatment system can process per hour. This is a dimensionless fluctuation adjustment coefficient used to control the correction magnitude of the overall fluctuation intensity to the processing time. To ensure that the volatility enhancement effect is non-linear but increases smoothly, H represents the overall volatility intensity.

[0039] Specifically, the formula indicates that the wastewater treatment time is determined not only by the total wastewater flow and wastewater treatment efficiency, but also by the additional burden on the treatment process caused by drastic fluctuations in wastewater flow over a short period of time. In other words, during abrupt changes, even if the total amount of wastewater remains unchanged, if the overall fluctuation intensity is large, the influence of flow instability on the wastewater treatment time can be reflected by nonlinearly amplifying the overall fluctuation intensity through the ln function, thereby lengthening the final calculated treatment time.

[0040] S4-2. Based on the sewage treatment time of M sudden sewage flow rates circulating in the sewage treatment equipment and the predefined minimum standard delay, determine the sudden discharge interval of the M sudden sewage flow rates through the sewage treatment equipment. Specifically, the variable discharge interval is used to represent the time range from the entry of the variable flow into the wastewater treatment equipment to the completion of effluent discharge. Its start time is the initial variable timestamp plus the shortest standard delay, and its end time is the start time plus the wastewater treatment duration. This variable discharge interval is the delayed manifestation of the variable flow in the treatment system.

[0041] S4-3. Calculate the output load of the interval based on the sudden change drainage interval and its corresponding sudden change total flow.

[0042] Specifically, the interval output load is equal to the total flow rate of the sudden change divided by the duration of the sudden drainage interval, and the unit can be L / min or m³ / h, etc.

[0043] In this embodiment, the treatment time is adjusted based on the comprehensive fluctuation intensity so that the sudden discharge interval can reflect the specific delay impact of flow fluctuation on the treatment process. The output load calculated by combining this interval with the total flow of the sudden change clarifies the unit time load intensity of sewage during the delayed release process.

[0044] For example, the application steps of the input load determination unit include: S5-1. Use a pre-trained ARIMA model to obtain the K delayed input sewage flow rates for the abrupt drainage interval; S5-2. Calculate the total sewage flow rate of the sudden drainage interval based on the K delayed input sewage flow rates of the sudden drainage interval; S5-2. Calculate the input load of the drainage interval based on the total sewage flow rate of the interval with sudden changes; Specifically, the interval input load is equal to the actual inflow intensity borne by the sudden drainage interval per unit time.

[0045] In this embodiment, by introducing the delayed input sewage flow rate within the sudden discharge interval and calculating the interval input load accordingly, a quantitative characterization of the pressure intensity borne by the treatment system during the "delayed release of sudden flow" stage is achieved. This breaks through the limitations of traditional methods that only evaluate based on the flow rate level at the moment of sudden occurrence, and more accurately monitors the input load faced by the system during the delayed treatment process.

[0046] For example, the application steps of the load status determination unit include: S6-1. Calculate the combined load of the abrupt drainage interval based on the interval output load and interval input load; Specifically, the overall load can be obtained by weighted summation of the interval output load and the interval input load. Furthermore, since the interval output load represents the purified discharge of wastewater during the treatment process, it usually has a relatively small immediate control pressure on the system, so its weight can be set to a low value (e.g., 0.15); while the interval input load directly reflects the initial wastewater surge entering the system per unit time, requiring priority response and adjustment from the system, therefore its weight should be significantly higher than that of the output load (e.g., 0.85).

[0047] S6-2. If the total load exceeds the set threshold, the wastewater treatment equipment is determined to be in an overload state. S6-3. If the sewage treatment equipment is determined to be in an overload state, calculate the response time between the current timestamp and the starting point of the sudden drainage interval, and perform hierarchical scheduling based on the response time.

[0048] For example, the hierarchical scheduling based on response time is a data rule-driven dynamic control process. The system divides the response time into several scheduling levels. For instance, when the response time is in the initial stage (e.g., less than threshold T1), it enters emergency mode, implementing external diversion or initiating flow restriction warnings; when the response time exceeds a medium threshold (e.g., T2), it adjusts the inlet gate or switches to a backup processing unit; if the response time continues to increase (e.g., exceeding T3), only the local buffer pool is activated. This hierarchical scheduling strategy can automatically adjust based on historical operating data and equipment pressure capacity, ensuring the processing system has flexible and controllable adjustment response capabilities under different load pressures.

[0049] In this embodiment, by weighting and fusing the input and output loads of the interval, a comprehensive load index is constructed. Combined with multi-level scheduling driven by response time, real-time monitoring and response linkage control of sewage treatment equipment based on operating pressure are realized.

[0050] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means.

[0051] The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g.,...), etc. DVD ( ), or semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0052] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Furthermore, the mutual couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0053] 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 that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A wastewater treatment flow online monitoring system, characterized in that, include: A sliding window unit is used to define a sliding future time window on the timeline; Sequence localization unit, used to locate abrupt changes in wastewater flow within a future time window; The intensity calculation unit is used to calculate the overall fluctuation intensity of the wastewater flow mutation sequence; The output load determination unit is used to dynamically determine the delayed abrupt drainage interval of the sewage flow mutation sequence on the time axis based on the comprehensive fluctuation intensity, as well as the interval output load of the abrupt drainage interval. The method of dynamically determining the delayed abrupt discharge interval of the sewage flow mutation sequence on the time axis based on comprehensive fluctuation intensity, and the interval output load of the abrupt discharge interval, includes: Based on the comprehensive fluctuation intensity, the total flow rate of the sudden change, and the predefined wastewater treatment efficiency, calculate the wastewater treatment time for M sudden changes in wastewater flow rate to circulate within the wastewater treatment equipment; The formula for calculating the wastewater treatment time is: ； in, Wastewater treatment time (unit: h) represents the total time required to treat sudden flow changes; The total wastewater flow rate (unit: m³) is within the abrupt change interval. Wastewater treatment efficiency (unit: m³ / h) represents the volume of wastewater that the wastewater treatment system can process per hour. β is a dimensionless fluctuation adjustment coefficient used to control the correction magnitude of the overall fluctuation intensity on the treatment time. To ensure that the fluctuation enhancement effect is non-linear but increases smoothly, H represents the overall fluctuation intensity; Based on the wastewater treatment time of M sudden wastewater flows circulating in the wastewater treatment equipment and the predefined minimum standard delay, determine the sudden discharge interval of the M sudden wastewater flows through the wastewater treatment equipment. Calculate the output load of the interval based on the abrupt drainage interval and its corresponding total abrupt flow. The input load determination unit is used to obtain K delayed input sewage flow rates in the sudden drainage interval and calculate the interval input load based on the K delayed input sewage flow rates. The load status determination unit is used to determine the load status based on the interval output load and interval input load, and to perform scheduling based on the load status.

2. The wastewater treatment flow online monitoring system according to claim 1, characterized in that, Define a sliding future time window on the timeline, including: S1-1, Mark the current timestamp on the timeline; S1-2. Starting from the current timestamp, advance the preset standard duration along the time axis to determine a target timestamp for monitoring sewage flow. S1-3. Define a future time window of fixed length between the current timestamp and the target timestamp; wherein the future time window covers N timestamps and is always aligned with the target timestamp.

3. The wastewater treatment flow online monitoring system according to claim 2, characterized in that, Within the future time window, locate the abrupt changes in wastewater flow sequences, including: S2-1. Using a pre-trained ARIMA model, obtain N predicted sewage flow rates within a future time window; S2-2. Based on the predicted N wastewater flow rates, mark the start and end timestamps of the abrupt change on the time axis. S2-3. Locate the sewage flow mutation sequence based on the start mutation timestamp and the end mutation timestamp.

4. The wastewater treatment flow online monitoring system according to claim 2, characterized in that, Based on the predicted N wastewater flows, mark the start and end timestamps of the abrupt change on the time axis, including: S2-2-1. Using a single timestamp as the step size, slide the future time window successively and update the corresponding N predicted sewage flows. S2-2-2. Extract the predicted sewage flow rate of the target timestamp and its adjacent timestamps from the N predicted sewage flow rates after each future time window slide. S2-2-3. Based on the predicted sewage flow rate of the target timestamp and its adjacent timestamps, calculate the instantaneous flow rate difference formed by each future time window sliding. S2-2-4. If the instantaneous flow difference is greater than the first threshold, then the target timestamp corresponding to this sliding is marked as the starting change timestamp; wherein, the first threshold is used to identify the positive change threshold for predicting the increase of sewage flow. S2-2-5. Continue executing steps S2-2-1 to S2-2-3. If the instantaneous flow difference is less than the second threshold, then mark the target timestamp corresponding to this sliding as the end of the mutation timestamp. The second threshold is used to identify the negative change threshold for predicting the decline in sewage flow.

5. The wastewater treatment flow online monitoring system according to claim 1, characterized in that, Based on the start and end timestamps of the mutation, the wastewater flow mutation sequence is located, including: S2-3-1. Anchor M consecutive mutation timestamps between the start mutation timestamp and the end mutation timestamp; S2-3-2. From N predicted wastewater flow rates, select M abrupt wastewater flow rates corresponding to M consecutive timestamps of abrupt changes. S2-3-3. Arrange the M sudden sewage flow rates in order of timestamp to construct a sudden flow rate sequence.

6. The wastewater treatment flow online monitoring system according to claim 1, characterized in that, Based on the wastewater flow mutation sequence, the comprehensive fluctuation intensity between the start and end mutation timestamps is calculated, including: S3-1. In the M abrupt changes in wastewater flow rate in the wastewater flow rate mutation sequence, calculate the M-1 flow rate differences between adjacent abrupt changes in wastewater flow rate. S3-2. Normalize the M-1 flow differences to obtain the standard flow difference set; S3-3. Calculate the variance of the standard flow difference set, and define the variance as the fluctuation intensity of the sewage flow mutation sequence; S3-4. Calculate the total flow rate of the sudden changes based on the M sudden changes in wastewater flow rate; S3-5. Calculate the average flow intensity based on the total flow of the mutation and the duration of the flow mutation between the start and end timestamps. S3-6. The average flow intensity and the fluctuation intensity are weighted and summed according to the set weights to obtain the comprehensive fluctuation intensity.

7. The wastewater treatment flow online monitoring system according to claim 1, characterized in that, Based on K delayed input sewage flow rates, calculate the interval input load corresponding to the abrupt drainage interval, including: S5-1. Use a pre-trained ARIMA model to obtain the K delayed input sewage flow rates for the abrupt drainage interval; S5-2. Calculate the total sewage flow rate of the sudden drainage interval based on the K delayed input sewage flow rates of the sudden drainage interval; S5-2. Calculate the input load of the interval based on the total sewage flow rate of the sudden drainage interval.

8. The wastewater treatment flow online monitoring system according to claim 1, characterized in that, Based on the interval output load and interval input load, determine the load status and perform scheduling based on the load status, including: S6-1. Calculate the combined load of the abrupt drainage interval based on the interval output load and interval input load; S6-2. If the total load exceeds the set threshold, the wastewater treatment equipment is determined to be in an overload state. S6-3. If the sewage treatment equipment is determined to be in an overload state, calculate the response time between the current timestamp and the starting point of the sudden drainage interval, and perform hierarchical scheduling based on the response time.

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