An on-line monitoring system for flow conditions in a sludge transport pipeline

Abstract

The present application relates to the technical field of online monitoring, in particular to a kind of sludge conveying pipeline flow state's online monitoring system, system includes: real-time parameter acquisition module, based on real-time monitoring sludge pump's operating parameter rotational speed, outlet pressure, inlet pressure and sludge conveying pipeline end or intermediate point's flow and pressure data, obtains current system operating parameter snapshot.In the present application, by real-time acquisition sludge pump rotational speed, outlet pressure, inlet pressure multidimensional operating parameter, and combined with the flow and pressure data of pipeline end or intermediate point, system operation snapshot is constructed, the state change in sludge conveying process can be captured;Then through inlet pressure and rotational speed data combined with sludge rheological property calculation theory hydraulic conveying parameter, introduce the geometric dimension parameter of conveying pipeline to deduce the flow prediction value of target monitoring point, so that the prediction benchmark with wider coverage is established in spatial dimension.

Description

Technical Field

[0001] This invention relates to the field of online monitoring technology, and in particular to an online monitoring system for the flow status of sludge conveying pipelines. Background Technology

[0002] Online monitoring technology is a comprehensive technology system based on sensors, data acquisition, signal processing, and real-time transmission to achieve continuous monitoring, dynamic evaluation, and intelligent early warning of the status of targets such as industrial equipment, environmental media, and operating conditions.

[0003] Current online monitoring technologies are limited to static acquisition and fluctuation alarms of current operating parameters, lacking the ability to comprehensively model the evolution trend of flow states. This is especially problematic when dealing with high-viscosity, strongly non-Newtonian fluids such as sludge, as the nonlinear effects of their rheological properties under complex pipeline structures are easily overlooked, making it difficult to accurately determine whether the transport is abnormal. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and to propose an online monitoring system for the flow status of sludge conveying pipelines.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an online monitoring system for the flow status of sludge conveying pipelines includes:

[0006] The real-time parameter acquisition module obtains a snapshot of the current system operating parameters based on real-time monitoring of the sludge pump's operating parameters, such as speed, outlet pressure, inlet pressure, and flow and pressure data at the end or middle of the sludge conveying pipeline.

[0007] The baseline state prediction module generates theoretical hydraulic transport parameters based on the inlet pressure and rotational speed data in the current system operating parameter snapshot and the rheological characteristics of sludge. Combining the theoretical hydraulic transport parameters with the geometric dimension data of the sludge transport pipeline, it calculates the predicted flow rate of the target monitoring point in the pipeline and establishes a pipeline parameter prediction baseline.

[0008] The operation deviation calculation module compares the flow rate at the end or middle point of the pipeline in the current system operation parameter snapshot with the corresponding predicted value in the pipeline parameter prediction benchmark item by item to obtain a multi-point parameter difference sequence. The multi-point parameter difference sequence is summarized to calculate the value reflecting the degree of inconsistency between the operating state and the predicted state of the sludge conveying pipeline, and the comprehensive operation deviation value is obtained.

[0009] The pipeline status assessment module compares the comprehensive operational deviation value with the preset acceptable deviation range for normal operation of the online monitoring system, generates a deviation exceeding the limit status, determines the current flow capacity level of the sludge conveying pipeline based on the deviation exceeding the limit status, and obtains the pipeline flow assessment signal.

[0010] Preferably, the steps for obtaining the current system operating parameter snapshot are as follows:

[0011] The speed, outlet pressure, and inlet pressure of the sludge pump, as well as the flow rate and pressure at the end or middle of the sludge conveying pipeline, are collected in real time. The data are recorded synchronously and a unified time stamp is established to obtain a set of real-time monitoring data.

[0012] Based on the real-time monitoring data set, the sludge pump speed, outlet pressure, and inlet pressure values, as well as the flow rate and pressure values ​​at the end or middle of the sludge conveying pipeline, are paired and combined according to a unified time stamp. Data items at different times caused by sensor response delays are eliminated to form a snapshot of the current system operating parameters.

[0013] Preferably, the steps for obtaining the theoretical hydraulic transport parameters are as follows:

[0014] Calculate the theoretical hydraulic transport parameters based on the current system operating parameter snapshot;

[0015] Based on the theoretical hydraulic transport parameters, the theoretical hydraulic transport parameters generated at different time points are summarized and analyzed to identify values ​​with abnormal fluctuations or deviations from the boundary, and unstable samples are removed to form the theoretical hydraulic transport parameters.

[0016] Preferably, the steps for obtaining the pipeline parameter prediction benchmark are as follows:

[0017] The theoretical hydraulic transport parameters and the geometric dimensions of the sludge transport pipeline were extracted. The units of the theoretical hydraulic transport parameters and the inner diameter data of the sludge transport pipeline were converted. The pipeline length data was standardized. The three types of data were matched according to the sampling time points to form a unified data set for hydraulic transport and pipeline structure.

[0018] Based on the unified data set of hydraulic transport and pipeline structure, the predicted flow rate of the target monitoring point in the pipeline is calculated.

[0019] Based on the predicted flow rate, the predicted flow rate values ​​for all target monitoring points are summarized in chronological order to generate a pipeline parameter prediction baseline.

[0020] Preferably, the steps for obtaining the multi-point parameter difference sequence are as follows:

[0021] Extract the flow rate at the end or middle point of the pipeline from the current system operating parameter snapshot to obtain the actual monitored flow rate sequence;

[0022] Based on the actual monitored flow sequence, retrieve the predicted flow that matches the corresponding time point and monitoring location in the pipeline parameter prediction benchmark, establish the correspondence between the monitored flow value and the predicted flow value item by item, and generate a list of monitoring and predicted flow matching pairs.

[0023] Based on the monitoring and predicted flow matching pair list, the difference between the actual monitored flow value and the predicted flow value for each pair is calculated sequentially to form a multi-point parameter difference sequence.

[0024] Preferably, the step of obtaining the comprehensive operating deviation value is as follows:

[0025] The flow difference values ​​of each monitoring point in the multi-point parameter difference sequence are called, all flow differences are arranged according to the time order of the monitoring points, and the minimum, maximum, average and standard deviation of each time period are calculated to generate a parameter difference statistical index group.

[0026] Based on the parameter difference statistical index group, the maximum difference value, minimum difference value and standard deviation value are extracted, the fluctuation range and central tendency between the values ​​are compared and analyzed, the overall deviation is extracted, and a value reflecting the degree of inconsistency between the operating status and the predicted status of the sludge conveying pipeline is generated.

[0027] Based on the value reflecting the degree of inconsistency between the operating status and the predicted status of the sludge conveying pipeline, it is determined whether the current difference distribution exceeds the anomaly judgment threshold, and the comprehensive operating deviation value is obtained based on the current difference characteristics.

[0028] Preferably, the step of obtaining the deviation exceeding the limit is as follows:

[0029] The deviation tolerance range of normal operation is called, the comprehensive operation deviation value is extracted, and the comprehensive operation deviation value is compared with the upper and lower limits of the deviation tolerance range item by item to confirm whether the current comprehensive operation deviation value is within the deviation tolerance range, and a preliminary deviation judgment mark is obtained.

[0030] Based on the preliminary deviation judgment identifier, calculate the magnitude of the comprehensive operational deviation value that exceeds the upper and lower limits of the deviation acceptance range, record the difference between the upper limit and the lower limit, determine the deviation value that exceeds the threshold, and generate the excess difference information.

[0031] Based on the above-limit difference information, it is determined whether there is a valid value for the above-limit difference information. If there is a valid value for the above-limit difference information, it is confirmed that the deviation of the sludge conveying pipeline has exceeded the acceptable deviation range. If there is no valid value, it is confirmed that the deviation of the pipeline operation status has not exceeded the limit, and a deviation exceeding the limit status is generated.

[0032] Preferably, the step of acquiring the pipeline flow assessment signal is as follows:

[0033] Based on the aforementioned deviation exceeding the limit, calculate the current flow capacity level of the sludge conveying pipeline;

[0034] Based on the current flow capacity level of the sludge conveying pipeline, a continuous interval comparison is performed with the set boundary values ​​of the three flow capacity levels. If the current flow capacity level is greater than the set upper limit threshold, a low flow capacity level flag is output; if it is in the middle range, a normal flow capacity level flag is output; if it is lower than the minimum threshold, it is marked as a high flow capacity level, and a pipeline flow assessment signal is generated.

[0035] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0036] This invention captures changes in sludge transport status by acquiring multi-dimensional operating parameters such as sludge pump speed, outlet pressure, and inlet pressure in real time, and constructing system operation snapshots by combining flow and pressure data from the pipeline end or intermediate points. Furthermore, it calculates theoretical hydraulic transport parameters using inlet pressure and speed data combined with sludge rheological properties, and derives flow prediction values ​​for target monitoring points by incorporating pipeline geometric parameters, thus establishing a broader prediction benchmark in the spatial dimension. Next, it compares the actual flow rate with the predicted value point-by-point, forming a multi-point parameter difference data set containing location sequences. After integrated analysis, a comprehensive operational deviation value is generated to measure the degree of deviation between the sludge transport operation status and the predicted status. By precisely comparing this deviation value with the acceptable deviation range, it determines whether there are operational anomalies and classifies the current flow capacity level accordingly. This improves the timeliness and accuracy of sludge transport status identification, enhances the lead time for pipeline blockage risk perception, and supports multi-point parallel evaluation and dynamic source tracing, solving the problems of delayed evaluation response and limited parameter coverage in traditional monitoring. Attached Figure Description

[0037] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0039] Please see Figure 1 This invention provides a technical solution: an online monitoring system for the flow status of sludge conveying pipelines, comprising:

[0040] The real-time parameter acquisition module obtains a snapshot of the current system operating parameters based on real-time monitoring of the sludge pump's operating parameters, such as speed, outlet pressure, inlet pressure, and flow and pressure data at the end or middle of the sludge conveying pipeline.

[0041] The baseline state prediction module generates theoretical hydraulic transport parameters based on the inlet pressure and rotational speed data in the current system operating parameter snapshot and the rheological characteristics of sludge. Combining the theoretical hydraulic transport parameters with the geometric dimensions of the sludge transport pipeline, it calculates the predicted flow rate at the target monitoring point in the pipeline and establishes a pipeline parameter prediction baseline.

[0042] The operation deviation calculation module compares the flow rate at the end or middle point of the pipeline in the current system operation parameter snapshot with the corresponding predicted value in the pipeline parameter prediction benchmark item by item to obtain a multi-point parameter difference sequence. The multi-point parameter difference sequence is summarized to calculate the value reflecting the degree of inconsistency between the operating status and the predicted status of the sludge conveying pipeline, and obtains the comprehensive operation deviation value.

[0043] The pipeline status assessment module compares the comprehensive operational deviation value with the preset acceptable deviation range for normal operation of the online monitoring system, generates deviation exceeding the limit status, determines the current flow capacity level of the sludge conveying pipeline based on the deviation exceeding the limit status, and obtains the pipeline flow assessment signal.

[0044] The steps to obtain a snapshot of the current system running parameters are as follows:

[0045] The speed, outlet pressure, and inlet pressure of the sludge pump, as well as the flow rate and pressure at the end or middle of the sludge conveying pipeline, are collected in real time. The data are recorded synchronously and a unified time stamp is established to obtain a set of real-time monitoring data.

[0046] Based on the real-time monitoring data set, the sludge pump speed, outlet pressure, and inlet pressure values, as well as the flow rate and pressure values ​​at the end or middle of the sludge conveying pipeline, are paired and combined according to a unified time stamp. Data items at different times caused by sensor response delays are eliminated to form a snapshot of the current system operating parameters.

[0047] Specifically, multi-source heterogeneous data is synchronously collected using sensors deployed on the sludge pump body and its inlet and outlet pipelines, as well as sensors selected at key monitoring points along the sludge transport pipeline (e.g., locations at 30%, 70%, and the end of the pipeline at distances from the pump outlet; these points typically represent sections where hydraulic conditions may change or are prone to siltation). Specifically, electromagnetic flowmeters are used to collect the instantaneous flow rate at specific cross-sections within the pipeline, with a preset range of 0 to 500 cubic meters per hour and an accuracy of ±0.5%. Piezoresistive pressure sensors are used to collect the pressure at various points, with a preset range of 0 to 1.6 MPa and an accuracy of ±0.25%. A rotary encoder or frequency converter feedback signal is used to obtain the real-time rotational speed of the sludge pump, with a measurement range set to 0 to 1500 revolutions per minute. The data acquisition frequency is uniformly set to once per second, i.e., the sampling period T. sample =1 second. This frequency is based on the analysis of historical operating data to determine its ability to capture fluctuations in sludge conveying conditions while avoiding data redundancy. All collected raw analog signals are converted into digital signals by the built-in or external signal conditioning circuits of their respective sensors. Subsequently, each independent digital signal (including rotational speed, outlet pressure, inlet pressure, flow rate at each monitoring point, and pressure at each monitoring point) is stamped with a unified, high-precision timestamp at the data aggregation node. This timestamp comes from the system clock calibrated by the Network Time Protocol (NTP) to ensure that the time base of all data points is consistent, and the timestamp accuracy reaches the millisecond level. These parameter values ​​with unified timestamps are aggregated in real time and stored in a temporary dynamic array or buffer to form a raw measurement dataset containing a time series, which is the real-time monitoring value set.

[0048] Based on the real-time monitoring data set obtained in the previous step, the system initiates a data pairing and alignment process. Its core objective is to precisely combine various parameter values ​​(specifically including sludge pump speed, outlet pressure, and inlet pressure, as well as flow rate and pressure values ​​at selected endpoints or intermediate points of the sludge transport pipeline, such as the aforementioned 30%, 70%, and endpoint positions) from different sensors into a single data record, according to a unified time stamp. To this end, a time window alignment tolerance Δt is set. align The tolerance Δt align The value is set to 0.5 seconds. This is based on a comprehensive evaluation of the inherent response times of each sensor in the system (e.g., pressure sensor response time is typically within 100 milliseconds, while flow meter response time may be in the range of 200-400 milliseconds) and the network latency of data transmission to the processing unit (typically estimated to be within 50 milliseconds), with a certain margin (e.g., 100 milliseconds) added. That is, Δt align= 400ms + 50ms + 50ms = 0.5s. During processing, the system uses the timestamp t of one of the core parameters (e.g., sludge pump speed) ref Based on this, other parameters are retrieved from the real-time monitoring data set in [t]. ref -Δt align / 2,t ref +Δt align / 2] For values ​​within the time window, if a unique value corresponding to the parameter is found within the window, it is paired with the baseline parameter; if multiple values ​​are found, the value with the closest timestamp t is selected. ref If some parameters are missing within a specified time window, then that t... ref The entire set of data at any given time may be marked as incomplete or directly discarded. The specific discarding strategy is as follows: if critical parameters such as pump speed or inlet pressure are missing, the combination is invalid; if non-critical intermediate pipeline parameters are missing, the corresponding item in the combination is empty, but the combination itself may be retained. This step effectively filters out data lost due to momentary interruptions in the transmission of individual sensor signals or response lags exceeding Δt. align The boundaries, or the data items with significantly inconsistent timestamps due to asynchronous processing of data acquisition units, ensure that all parameters in each record after combination can represent the system state at similar times. By performing the above pairing and filtering operations on the data of all time points in the real-time monitoring data set, a series of structured, time-aligned multidimensional data records are finally formed, namely, the current system operating parameter snapshot.

[0049] The steps for obtaining theoretical hydraulic transport parameters are as follows:

[0050] Based on the current system operating parameter snapshot, calculate the theoretical hydraulic transport parameters using the following formula:

[0051]

[0052] Where H″ represents the theoretical hydraulic transport parameter, P e Indicates the inlet pressure value, μ c R represents the viscosity factor value corresponding to the c-th operating condition. s Indicates the rotational speed value;

[0053] Based on the theoretical hydraulic transport parameters, the theoretical hydraulic transport parameters generated at different time points are summarized and analyzed to identify values ​​with abnormal fluctuations or deviations from the boundary, and unstable samples are removed to form the theoretical hydraulic transport parameters.

[0054] Specifically, the formula: By correlating inlet pressure with rotational speed and viscosity factor under specific operating conditions, a standardized theoretical hydraulic transport parameter H″ is provided. This parameter reflects the system's theoretical transport capacity or hydraulic efficiency benchmark under current sludge characteristics and pump operating conditions, unaffected by fluctuations in the absolute value of a single parameter. By tracking changes in H″, potential operational deviations of the system can be identified more sensitively, such as initial pipe blockages or decreased pump efficiency, which may not be immediately apparent in a single pressure or rotational speed reading.

[0055] P e The steps to obtain the parameters are: P e This represents the inlet pressure value of the sludge pump, which is directly extracted from the "Current System Operating Parameter Snapshot" generated in the preceding steps. Each record in the "Current System Operating Parameter Snapshot" contains the measurement value from the sludge pump inlet pressure sensor paired with a uniform time stamp. For example, at a specific time point t... i Read the inlet pressure value P at that moment from the snapshot. e (t i This pressure is typically monitored continuously by a piezoresistive or capacitive pressure sensor installed on the sludge pump's suction line and recorded by a data acquisition system. To ensure data accuracy, the sensor is calibrated periodically. For example, at time point t1, the inlet pressure P is obtained from the "Current System Operating Parameters Snapshot". e It is 0.2 MPa, or 200,000 Pa.

[0056] μ c The steps to obtain the parameter are: μ c This represents the viscosity factor value corresponding to the c-th operating condition. It is an empirical coefficient, measured in Pascals per minute (Pa / RPM), derived from actual operating conditions (primarily sludge properties such as solids content and temperature), and calibrated to quantify the impact of different sludge characteristics on hydraulic transport behavior. First, different operating condition groups c are defined, for example, based on sludge solids content (SC, in percentage): Group 1 (c=1): SC < 2%; Group 2 (c=2): 2% ≤ SC < 5%; Group 3 (c=3): SC ≥ 5%. The current sludge solids content is monitored in real-time using an online solids concentration meter. Then, for each operating condition group c, during system commissioning or calibration, the corresponding μ is determined through a series of experiments. c Value. The specific method is as follows: Under a certain operating condition group c, adjust the pump speed R. s Record the inlet pressure P during multiple stable operating cycles, including the opening degree of the system outlet valve. e and rotational speed R sData is collected, and the sludge transport efficiency is evaluated (e.g., by comparing the actual flow rate with the theoretical flow rate, or by observing the system's energy consumption ratio). A set of parameters is selected that makes the theoretical hydraulic transport parameter H″ fall within an ideal reference value (e.g., H″). ref =1.0, indicating that the hydraulic matching is relatively good at this time. e and R s Combination, via μ c =P e / (H″ ref ·R s The μ of this condition set was calculated. c For example, the current sludge solids content is 3.5%, belonging to group 2 (c=2). Under these conditions, after calibration tests, the system operates optimally when the inlet pressure is 220000 Pa and the rotational speed is 1000 RPM. At this point, H″ is set... ref =1.0, then μ is calculated. c＝2 =220000Pa / (1.0·1000RPM) = 220Pa / RPM. This value of 220Pa / RPM is the μ used under the current operating conditions. c .

[0057] R s The steps to obtain the parameters are: R s This indicates the rotational speed of the sludge pump, expressed in revolutions per minute (RPM). This value is also extracted directly from the "Current System Operating Parameter Snapshot," which includes real-time measurements from a sludge pump speed sensor paired with a uniform time stamp. The speed is typically obtained via a rotary encoder mounted on the pump motor shaft or through feedback signals from the frequency converter. These sensors provide accurate speed readings and transmit them to the data acquisition system. For example, in conjunction with the aforementioned P... e At the same time point t1, the pump speed R obtained from the "Current System Operating Parameter Snapshot" s It is 900 RPM.

[0058] Calculation process: Based on the aforementioned parameter acquisition steps, the following example value is obtained: Inlet pressure P e =200000Pa. The current sludge solids content is 4%, belonging to the defined operating condition group c=2, corresponding to a viscosity factor μ. c =220Pa / RPM. Rotational speed R s =900RPM.

[0059] Substitute these values ​​into the formula

[0060]

[0061] H″≈1.0101;

[0062] The results show that when the inlet pressure is 200,000 Pa, the rotation speed is 900 RPM, and the viscosity factor corresponding to the current operating conditions (such as sludge solids content of 4%) is 220 Pa / RPM, the calculated theoretical hydraulic transport parameter H″ is approximately equal to 1.0101.

[0063] Based on a series of theoretical hydraulic transport parameters H″ calculated at different time points, i.e., a set of H″ values ​​arranged in chronological order, such as [H″1,H″2,...,H″] N The system then summarizes, statistically analyzes, and processes these parameters to identify and eliminate abnormal data points caused by measurement noise, transient disturbances, or atypical operating conditions. First, the system uses a sliding window method to calculate the statistical characteristics of the H″ value sequence, setting a sliding window length, for example, W = 20 consecutive data points. For the H″ data within the window, its arithmetic mean (H″) is calculated. avg ) and standard deviation (H″) std Then, abnormal fluctuation values ​​are identified according to preset anomaly judgment rules. The rule is: if a certain H″ value exceeds the range of the average value of its window plus or minus a preset multiple of the standard deviation, it is judged as an abnormal fluctuation. For example, the judgment threshold is set to H″. avg ±k·H″ std The value of k is set empirically, generally k=3. This k value is based on the normal distribution theory, which states that approximately 99.7% of the data should fall within the mean ± 3 standard deviations. Data points outside this range can be considered low-probability events, i.e., outliers. Specifically, if H″ i >H″ avg,i +3·H″ std,i or H″ i <H″ avg,i -3·H″ std,i Then H″ i If an error is detected, the system will also check whether the H″ value deviates from the preset absolute boundary. These boundary values ​​are obtained based on statistical analysis of a large amount of historical stable operating data or the theoretical performance curve of the pump, and represent the reasonable upper limit of H″ fluctuation under normal operating conditions. upper and lower limit H″ lower For example, by statistically analyzing the H″ values ​​under all working conditions over the past month, the 5th percentile is taken as the lower limit (H″). lower =0.6), the 95th percentile is the upper limit (H″). upper=2.2), if a certain H″ value is less than 0.6 or greater than 2.2, it is also identified as an outlier that deviates from the boundary. All H″ numerical samples identified as having abnormal fluctuations or deviating from the boundary are considered as unstable samples and removed from the time series. They do not participate in the subsequent calculation of the baseline state prediction module. After the above screening process, the remaining H″ numerical series is considered to be stable and valid, thus forming a set of theoretical hydraulic transport parameters that has been purified.

[0064] The steps for obtaining the baseline for pipeline parameter prediction are as follows:

[0065] The theoretical hydraulic transport parameters and the geometric dimensions of the sludge transport pipeline were extracted. The units of the theoretical hydraulic transport parameters and the inner diameter data of the sludge transport pipeline were converted. The pipeline length data was standardized. The three types of data were matched according to the sampling time points to form a unified data set for hydraulic transport and pipeline structure.

[0066] Based on the unified data set for hydraulic transport and pipeline structure, the predicted flow rate at the target monitoring point within the pipeline is calculated using the following formula:

[0067]

[0068] Among them, Q pred,f D represents the predicted flow rate. z H represents the inner diameter of the z-th sludge transport pipeline, H″ represents the previously calculated dimensionless theoretical hydraulic transport parameters, and R... s L represents the rotational speed at that position. y This represents the transmission length of the y-th segment of the sludge conveying pipeline;

[0069] Based on the flow forecast values, the flow forecast values ​​of all target monitoring points are summarized in chronological order to generate a pipeline parameter prediction baseline.

[0070] Specifically, the system extracts time-series data from the purified set of "theoretical hydraulic transport parameters" obtained in previous steps. This data consists of a series of dimensionless H″ values ​​and their corresponding timestamps. Simultaneously, the system retrieves "geometric dimension data of the sludge transport pipeline" pre-stored in the configuration database. This data is entered after system initialization or changes to the pipeline system and typically originates from pipeline engineering design drawings or is obtained through on-site measurement methods such as laser scanning and ultrasonic thickness measurement. The data includes the inner diameter D of each pipeline section. z (For example, a main pipeline may consist of multiple pipe sections with different nominal diameters, such as DN200 and DN250, connected in series, with their precise inner diameters recorded separately) and the transmission length L of each corresponding pipe section. y(For example, the first DN250 pipe section is 150 meters long, and the second DN200 pipe section is 200 meters long.) Next, these data are preprocessed to ensure unit consistency and standardized format. Specifically, all pipe inner diameter data, if originally recorded in millimeters (mm) or inches (in), are uniformly converted to meters (m). For example, if the inner diameter of a DN250 pipe section is recorded as 250mm, it is converted to 0.25m. All pipe length data, if originally recorded in kilometers (km) or feet (ft), are also uniformly converted to meters (m). For example, if the length of a pipe section is recorded as 0.2km, it is converted to 200m. This is the "standardization processing of pipe length data." The "theoretical hydraulic transport parameter" H″ itself is dimensionless and does not require unit conversion, but it is necessary to ensure that its timestamp matches the real-time rotational speed R involved in subsequent calculations. s The timestamps (also derived from "snapshot of current system operating parameters") can be aligned. Finally, the system will use the purified, time-series-based theoretical hydraulic transport parameters H″ and the corresponding real-time rotational speed R at the specified timestamps. s And the converted and standardized static pipe geometry data (D of each pipe segment) z and L y Integrate H″(t) and R based on a common timestamp. s (t) Pairing and associating with the set of pipeline geometry parameters describing the current transport path forms a "unified data set for hydraulic transport and pipeline structure". This data set is represented as a series of records, each containing a timestamp, H″ value, R s The value and the inner diameter and length information of one or more pipe sections on which the flow prediction at that moment is based.

[0071] formula: It should be noted that, to ensure consistency of physical units and to obtain the result in cubic meters per second (m²), 3 The predicted flow rate Q is expressed in units of / s. pred,f The rotational speed R in the formula s Revolutions per second (RPS) must be used as the unit. If the RPS is obtained from the "Unified Data Set for Hydraulic Transport and Pipeline Structures",... s If the unit is revolutions per minute (RPM), then conversion should be performed first: R s (RPS) = R s (RPM) / 60.

[0072] The formula combines the key geometric dimensions of the pipe (inner diameter D) z and length L y Estimate under specific hydraulic drive conditions (by H″ and R) s (Characteristic) The theoretical flow rate that can be achieved within the pipeline. The terms indicate that flow rate is extremely sensitive to the pipe's inner diameter, consistent with the fundamental physical laws governing fluid flow within pipes. H″, as a previously calculated composite hydraulic parameter, already incorporates the effects of inlet pressure and sludge viscosity factor, enabling the model to adapt to different sludge characteristics and pump inlet conditions. This formula is designed to provide a relatively simple benchmark for flow rate prediction, used for comparison with actual monitored flow rates to assess the pipe's flow status, rather than directly and precisely simulating complex sludge flow.

[0073] D z The steps to obtain the parameters are: D z This represents the inner diameter of the z-th sludge transport pipeline, in meters (m). This data originates from the "Unified Data Set for Hydraulic Transport and Pipeline Structure" formed in the preceding steps, specifically a portion of its "Geometric Dimension Data of Sludge Transport Pipelines." These geometric dimensions have been converted to meters during system configuration. The pipeline system may consist of multiple pipe segments with different inner diameters, labeled as segment 1, segment 2, ..., segment z. For example, a target monitoring point f might be located at the end of a pipe segment consisting of a single diameter, or at the end of a pipeline containing multiple series segments. When calculating the flow rate at a specific monitoring point, D... z The value is taken as the inner diameter of the pipe section directly connected upstream of the monitoring point or that plays a dominant role in its hydraulic characteristics. For example, if we consider a pipe with a nominal diameter of DN250 (actual inner diameter approximately 250mm), then D... z =0.25m.

[0074] The steps for obtaining the H″ parameter are as follows: H″ represents the dimensionless theoretical hydraulic transport parameter calculated previously. This parameter value is directly extracted from the "Unified Data Set for Hydraulic Transport and Pipeline Structures" and is the calculation result for a specific time point t. In the previous steps, the formula H″ = P... e / (μ c ·R s The result is calculated and outlier removal has been performed. For example, in the previous calculation example, H″≈1.0101 was obtained at a specific time point. This value will be used for the current traffic prediction calculation.

[0075] R s The steps to obtain the parameters are: R s This represents the sludge pump rotational speed at the same time point as the currently calculated theoretical hydraulic transport parameter H″. This data is also extracted from the "Unified Data Set for Hydraulic Transport and Pipeline Structures," and the original unit is typically revolutions per minute (RPM). This is used in the current flow prediction formula Q. pred,f And obtain m 3 The flow rate in units of / s needs to be converted to revolutions per second (RPS). The conversion formula is R... s (RPS) = R s(RPM) / 60. For example, in the previous calculation example, the rotational speed at the corresponding time point was 900 RPM, then R used in this formula... s =900 / 60=15RPS.

[0076] L y The steps to obtain the parameters are: L y This represents the transmission length of the y-th segment of the sludge transport pipeline, in meters (m). (This is related to D...) z Similarly, this data comes from the "Geometric Dimensions of Sludge Transport Pipelines" in the "Unified Data Group for Hydraulic Transport and Pipeline Structures," and the units have been standardized. It refers to the length of a specific pipe segment considered when calculating flow predictions. For cases where sludge is transported to the monitoring point via a single, uniform pipe segment, L... y This is the length of the pipe segment. For example, considering a pipe segment with a length of 100m, then L... y =100m.

[0077] Calculation process: Based on the aforementioned parameter acquisition steps, the following example value is obtained: Pipe inner diameter D z =0.25m. Dimensionless theoretical hydraulic transport parameter H″ = 1.0101. Rotational speed R s = 15 RPS (originally 900 RPM). Pipeline transmission length L y =100m. Constant π≈3.14159. Constant 128.

[0078] Substitute these values ​​into the formula

[0079]

[0080] Q pred,f ≈0.0000145263m 3 / s;

[0081] Convert to more commonly used units, such as cubic meters per hour (m³ / h). 3 / h): Q pred,f ≈0.0000145263·3600m 3 / h≈0.05229m 3 / h.

[0082] The results indicate that, given the pipe geometry (inner diameter 0.25 m, length 100 m), theoretical hydraulic transport parameters (H″≈1.0101), and pump speed (15 RPS), the predicted theoretical flow rate at the target monitoring point is approximately 0.0000145263 m³ / h. 3 / s or 0.05229m 3 / h. This predicted flow value Q pred,fThis will be the flow rate that should be achieved in the pipeline under ideal or baseline conditions. If the actual monitored flow rate is significantly lower than this value, it may indicate problems such as blockage, leakage, or decreased pump efficiency in the pipeline.

[0083] Based on the flow prediction value Q obtained in the previous step for each preset target monitoring point at a specific time point, pred,f The system then executes a summary procedure. Here, "target monitoring points" refer to several key locations selected along the sludge transport pipeline, such as a certain distance after the pump outlet, before major branch points, at the end of easily clogged sections, and at the end of the pipeline system. These locations are all equipped with actual flow monitoring instruments to compare the predicted values ​​with the measured values. Specifically, the summary process involves the system iterating through each target monitoring point and collecting the predicted flow value Q generated at all processed timestamps. pred,f (t), and then these predicted values ​​are organized and arranged in chronological order according to timestamp t. For each target monitoring point f, an independent time series data will be formed, in the form of [(t1,Q pred,f (t1)),(t2,Q pred,f (t2)),...,(t N Q pred,f (t N ))], where t1, t2, ..., t N Representing continuous sampling or calculation time points, this process ensures that the flow prediction history of each monitoring point is completely recorded and stored in an orderly manner. Finally, the time series set of flow prediction values ​​of all target monitoring points, arranged in chronological order, is integrated to form a structured dataset, which is the "pipeline parameter prediction baseline". This baseline will be used to compare with the actual flow data of each monitoring point collected in real time to evaluate the deviation between the actual operating state of the pipeline and the theoretical prediction state.

[0084] The steps to obtain the multi-point parameter difference sequence are as follows:

[0085] Extract the flow rate at the end or middle point of the pipeline from the current system operating parameter snapshot to obtain the actual monitored flow rate sequence;

[0086] Based on the actual monitored flow sequence, retrieve the predicted flow that matches the corresponding time point and monitoring location in the pipeline parameter prediction benchmark, establish the correspondence between the monitored flow value and the predicted flow value item by item, and generate a list of monitoring and predicted flow matching pairs.

[0087] Based on the list of monitoring and predicted flow matching pairs, the difference between the actual monitored flow value and the predicted flow value for each pair is calculated in turn to form a multi-point parameter difference sequence.

[0088] Specifically, the system first accesses the "current system operating parameter snapshot" constructed in the previous steps. This snapshot is a time-aligned dataset containing precise values ​​for multiple parameters, including the operating parameters of the sludge pumps at each preset monitoring time point, as well as flow rate and pressure along the pipeline (including the pipeline end and, for example, midpoints at 30% and 70% of the total pipeline length, respectively, from the pump outlet). Next, the system selectively extracts the actual flow rate values ​​measured at each specified monitoring point (e.g., the end discharge monitoring point, midpoint 1, and midpoint 2) at each timestamp. This flow rate data is collected in real-time by devices such as electromagnetic or ultrasonic flow meters installed at the corresponding monitoring locations and recorded in the snapshot, with the unit uniformly set to cubic meters per second (m³ / s). 3 The extraction process iterates through each record in the "Current System Operating Parameter Snapshot" ( / s), and for each preset monitoring point identifier used for flow comparison (e.g., "End Point", "Intermediate Point A"), it detects the corresponding flow reading and its associated timestamp. For example, if the snapshot contains the record {timestamp: "2023-10-26T10:00:00Z", ..., intermediate point A flow: 0.045m 3 If we use the sequence ` / s, ...}`, then we can extract the timestamp "2023-10-26T10:00:00Z" and the traffic value 0.045m. 3 / s represents the actual monitored flow rate at intermediate point A at that moment. After performing this extraction operation on the flow rate data of all timestamps and all specified monitoring points, the system will generate an independent time series for each monitoring point. This series consists of pairs of timestamps and corresponding actual monitored flow rate values. These independent series together constitute the "actual monitored flow rate series".

[0089] Based on the "actual monitored flow sequence" obtained in the previous step, and the "pipeline parameter prediction baseline" generated in an earlier stage (which includes the predicted flow value Q of each target monitoring point at each time point), pred,f (t)), the system begins to retrieve and match predicted traffic. Specifically, the system iterates through each record in the "Actual Monitoring Traffic Sequence," which contains the traffic data of a specific monitoring point f at a specific timestamp t. i Actual monitored flow rate Q actual,f (t i For such a record, the system uses its monitoring point identifier f and timestamp t. i As a query condition, a search is performed in the "Pipeline Parameter Prediction Baseline" dataset, with the goal of finding a monitoring point with the exact same identifier f and timestamp t. i Predicted flow value Q pred,f (t iThe matching here requires an absolutely precise correspondence of timestamps; that is, the two timestamps must be exactly the same. For example, if there is an item in the "Actual Monitoring Flow Sequence" that reads "Monitoring Point: 'Intermediate Point A'", "Timestamp: '2023-10-26T10:01:00Z'", "Actual Flow: 0.042m", then the matching is incorrect. 3 If / s}, the system will search for the entry {Monitoring point: "Intermediate point A", Timestamp: "2023-10-26T10:01:00Z"} in the "Pipeline Parameter Prediction Baseline" to obtain its corresponding predicted flow value, such as Q. pred,中间点A ("2023-10-26T10:01:00Z")=0.044m 3 If, for a given actual monitored flow data point, no predicted flow with the same monitoring point identifier and timestamp can be found in the "Pipeline Parameter Prediction Baseline" (possibly due to differences in data acquisition cycles or the prediction model not covering the exact time point), then that actual monitored flow data point will not participate in subsequent matching pair generation. Only when the actual monitored flow and the predicted flow can be successfully matched at both the monitoring location and time point will the system combine these values. After establishing the correspondence of all matching items item by item, a "Monitoring and Predicted Flow Matching Pair List" is formed. Each item in this list contains a timestamp, a monitoring point identifier, the actual monitored flow value of that point, and the corresponding predicted flow value.

[0090] Based on the "monitoring and predicted traffic matching pair list" generated in the previous step, this list consists of a series of records containing timestamps, monitoring point identifiers, actual monitored traffic values, and corresponding predicted traffic values. The system then processes each pair of traffic data in the list, calculating the difference between them sequentially. The calculation method is as follows: for any matching pair record in the list, for example, {timestamp: t...} i Monitoring point identifier: f, Actual monitored flow rate: Q actual,f (t i Predicted flow: Q pred,f (t i The system will extract the actual monitored flow rate value Q. actual,f (t i ) and predicted flow value Q pred,f (t i The difference between the two is calculated, specifically defined as the actual monitored flow minus the predicted flow, i.e., the flow difference ΔQ. f (t i )=Q actual,f (t i )-Q pred,f (t i This difference ΔQ f (t i The unit of flow rate is consistent with the unit of volume, namely cubic meters per second (m³ / s).3 The actual flow rate is calculated as follows: A positive value indicates that the actual flow rate is greater than the predicted flow rate, a negative value indicates that the actual flow rate is less than the predicted flow rate, and a zero value indicates that the two are exactly the same. The system iterates through all entries in the "Monitoring Predicted Flow Rate Matching Pair List," performs the same difference calculation operation on each entry, and stores each calculated flow rate difference along with its corresponding timestamp and monitoring point identifier. For example, if a matching pair is {timestamp: "2023-10-26T10:02:00Z", monitoring point: "end point", actual flow rate: 0.038m}... 3 / s, predicted flow rate: 0.040m 3 If the flow rate difference is calculated to be 0.038 - 0.040 = -0.002 m³ / s, then the calculated flow rate difference is 0.038 - 0.040 = -0.002 m³ / s. 3 / s, the calculation result, together with the timestamp and monitoring point identifier (“end point”), is used as a new data item. By summarizing the flow difference data items calculated from all monitoring points at all timestamps, a “multi-point parameter difference sequence” is finally formed.

[0091] The steps for obtaining the comprehensive operational deviation value are as follows:

[0092] The flow difference values ​​of each monitoring point in the multi-point parameter difference sequence are called, all flow differences are arranged according to the time order of the monitoring points, and the minimum, maximum, average and standard deviation of each time period are calculated to generate a parameter difference statistical index group.

[0093] Based on the parameter difference statistical index group, the maximum difference value, minimum difference value and standard deviation value are extracted. The fluctuation range and central tendency of the values ​​are compared and analyzed to extract the overall deviation and generate a value reflecting the degree of inconsistency between the operating status and the predicted status of the sludge conveying pipeline.

[0094] Based on the value reflecting the degree of inconsistency between the operating status and the predicted status of the sludge conveying pipeline, it is determined whether the current difference distribution exceeds the anomaly judgment threshold, and the comprehensive operating deviation value is obtained based on the current difference characteristics.

[0095] Specifically, the system first retrieves the "multi-point parameter difference sequence" generated in the previous steps. This sequence contains the flow difference (the difference between the actual monitored flow and the predicted flow, ΔQ) at different timestamps for each specified monitoring point (e.g., the monitoring point at the end of the pipeline, intermediate point A, intermediate point B, etc.). f(t)) Next, for statistical analysis, the system groups these flow difference data according to their corresponding monitoring point identifiers, and arranges the flow difference data under each monitoring point name in chronological order according to timestamps, forming a flow difference time subsequence for each monitoring point. Subsequently, for each monitoring point's flow difference time subsequence, the system selects data within a preset analysis time window. For example, it selects all flow difference data within the most recent calculation period (such as the past 30 minutes or the most recent 100 data sampling points), or the difference data of a fixed, representative historical time period, and analyzes the flow difference data within the selected time window. The system uses standard statistical methods to calculate the minimum value of the difference data set, i.e., to find the minimum reading of the flow difference within the time period; calculate the maximum value, i.e., to find the maximum reading of the flow difference within the time period; calculate the arithmetic mean; and calculate the standard deviation. These calculation processes do not change the original physical unit of the difference data (e.g., cubic meters per second). By performing the same statistical calculations on the flow difference sequence of each monitoring point, the system generates a set of statistical data containing the minimum, maximum, average, and standard deviation for each monitoring point. The collection of statistical data calculated separately for each monitoring point is then summarized to form the "parameter difference statistical index group".

[0096] Based on the "parameter difference statistical index group" generated in the previous step, this index group contains statistical information such as the minimum, maximum, average, and standard deviation of the flow difference at each monitoring point within a specific time period. The system then analyzes these statistical indicators to quantify the degree of inconsistency between the operating state and the predicted state. Specifically, for each monitoring point, the system extracts the maximum, minimum, and standard deviation of the flow difference from its corresponding statistical indicators. Then, by comprehensively evaluating these extracted values, the system analyzes the fluctuation range and central tendency of the difference data. The fluctuation range can be reflected by the distance between the maximum and minimum differences (i.e., the range), or by directly referring to the size of the standard deviation. A larger standard deviation or a larger range usually indicates that the flow difference changes drastically within that time period and has poor stability. The central tendency is mainly judged by the average value of the flow difference; its absolute value reflects the actual flow difference. To predict the degree of deviation of the average flow rate, the system combines these two aspects. For example, it calculates a comprehensive inconsistency score, which is obtained by weighting the absolute value of the average difference and the standard deviation of the difference according to preset weights. For example, the inconsistency score = weight coefficient 1 × (absolute value of the average difference) + weight coefficient 2 × (standard deviation of the difference), where weight coefficient 1 and weight coefficient 2 are both positive numbers and their sum is 1 (for example, weight coefficient 1 is set to 0.6 and weight coefficient 2 is set to 0.4). These weight coefficients are set based on historical operating data analysis and expert experience, reflecting the relative importance of the magnitude and stability of the average deviation in assessing the overall inconsistency. By calculating such an inconsistency score for each monitoring point, a set of values ​​quantifying the degree of inconsistency of each monitoring point is finally obtained. This is the set of "quantities reflecting the degree of inconsistency between the operating status and the predicted status of the sludge conveying pipeline".

[0097] Based on the set of "quantities reflecting the degree of inconsistency between the operating status and predicted status of the sludge conveying pipeline" generated in the previous step (i.e., the inconsistency score calculated for each monitoring point), the system further processes the data to obtain a single "comprehensive operational deviation value." This process first requires comparing the inconsistency score of each monitoring point with its respective "anomaly judgment threshold." This "anomaly judgment threshold" is a pre-set upper limit for the inconsistency score. For example, for the aforementioned inconsistency score, its threshold value can be set to 0.005 (e.g., the unit of the score result is equivalent to the flow rate unit cubic meters per second). The determination of this threshold value is based on statistical analysis of the inconsistency scores calculated during the long-term stable operation of the pipeline. For example, the 95th percentile of the score in historical normal data can be selected, or it can be determined by the process engineer according to the allowable process deviation range. The judgment process is as follows: if the inconsistency score calculated for a certain monitoring point is... If the consistency score exceeds the preset anomaly threshold, the difference distribution of the monitoring point is considered to have deviated abnormally. After judging the inconsistency scores of all monitoring points, the system "takes the current difference characteristics as the standard" and uses the maximum value of the inconsistency scores of all monitoring points as the "comprehensive operational deviation value" of the entire sludge transport pipeline system. For example, if the inconsistency scores of three monitoring points are 0.003, 0.006 and 0.004 respectively, and the anomaly threshold is 0.005, then since 0.006 exceeds 0.005, the system judges that there is an anomaly and uses 0.006 as the "comprehensive operational deviation value" for the current period. If the inconsistency scores of all monitoring points do not exceed their anomaly thresholds, the "comprehensive operational deviation value" can be the maximum value of the inconsistency scores of all monitoring points, or a specific baseline value representing "normal" (e.g., 0 or the average value within the threshold) can be assigned according to the rules.

[0098] The steps for obtaining information on deviation exceeding limits are as follows:

[0099] Call the acceptable deviation range of normal operation status, extract the comprehensive operation deviation value, compare the comprehensive operation deviation value with the upper and lower limits of the acceptable deviation range item by item, confirm whether the current comprehensive operation deviation value is within the acceptable deviation range, and obtain the preliminary deviation judgment mark.

[0100] Based on the initial deviation judgment mark, calculate the magnitude of the comprehensive operational deviation value that exceeds the upper and lower limits of the deviation acceptance range, record the difference between the upper limit and the lower limit, determine the deviation value that exceeds the threshold, and generate the excess difference information.

[0101] Based on the excess difference information, it is determined whether there is a valid value for the excess difference information. If there is a valid value for the excess difference information, it is confirmed that the deviation of the sludge conveying pipeline has exceeded the acceptable deviation range. If there is no valid value, it is confirmed that the deviation of the pipeline operation status has not exceeded the limit, and a deviation exceeding the limit status is generated.

[0102] Specifically, the "acceptable deviation range for normal operation" is first retrieved from the preset system configuration parameters. This range is set for the "comprehensive operational deviation value," and its upper and lower limits are determined based on statistical analysis of the "comprehensive operational deviation value" generated by the sludge transport pipeline under recognized normal and efficient operating conditions over a historical period (e.g., six months of continuous and stable operation). Specifically, the "comprehensive operational deviation value" calculated daily or per shift during the statistical period constitutes a dataset. The 5th percentile and 95th percentile of this dataset (or an interval jointly determined by domain experts based on acceptable minimum and maximum process fluctuations) are set as the lower and upper limits of the "acceptable deviation range," respectively. For example, if historical data shows that the normal "comprehensive operational deviation value" typically fluctuates between 0 and 0.008 (e.g., this value is dimensionless or...), then the deviation range is considered acceptable. If the deviation has a specific unit, the lower limit of the acceptable deviation range can be set to 0, and the upper limit to 0.008. Then, the system extracts the current "comprehensive operating deviation value" obtained in the previous calculation step and compares this current value with the upper and lower limits of the retrieved "acceptable deviation range". The judgment logic is as follows: if the current "comprehensive operating deviation value" is greater than or equal to the lower limit of the "acceptable deviation range" and less than or equal to its upper limit, then the current deviation is determined to be within the acceptable range. Otherwise, if the current "comprehensive operating deviation value" is less than the lower limit or greater than the upper limit, then it is determined to be outside the acceptable range. Based on this comparison result, the system generates a "preliminary deviation judgment identifier". For example, if the current "comprehensive operating deviation value" is 0.006 and the acceptable range is [0, 0.008], then the identifier is "within the range". If it is 0.009, then the identifier is "beyond the upper limit".

[0103] Based on the "preliminary deviation judgment identifier" generated in the previous step, the "comprehensive operational deviation value" within the current calculation period, and the determined "acceptable deviation range for normal operating conditions" (including the lower and upper limits), the system will next quantify the specific degree to which the deviation exceeds the limit. This calculation step is only activated when the "preliminary deviation judgment identifier" explicitly indicates that the current "comprehensive operational deviation value" is not within the acceptable range (e.g., identified as "exceeding the upper limit" or "below the lower limit"). If it is determined to be "exceeding the upper limit," the system will calculate the specific value by which the current "comprehensive operational deviation value" exceeds the upper limit of the "acceptable deviation range." The calculation method is: "the difference in value exceeding the upper limit" equals "comprehensive operational deviation value" minus "the upper limit of the acceptable deviation range." For example, if the "comprehensive operational deviation value" is 0.009, and the "upper limit of the acceptable deviation range" is 0.008, then the "the difference in value exceeding the upper limit" is 0.009 - 0.008. =0.001, this 0.001 is the determined "deviation value exceeding the threshold", and the nature of this difference is recorded as "exceeding the upper limit". If it is determined to be "below the lower limit" (this situation may occur less often when the "comprehensive operating deviation value" is usually non-negative, unless the acceptable lower limit is set to a specific value greater than zero), the system calculates the specific value by which the current "comprehensive operating deviation value" is lower than the lower limit of the "deviation acceptable range". The calculation method is: "the difference in value below the lower limit" equals the "lower limit of the deviation acceptable range" minus the "comprehensive operating deviation value". This calculation result is also used as the "deviation value exceeding the threshold", and its nature is recorded as "below the lower limit". If the "preliminary deviation judgment identifier" is "within the range", then no over-limit calculation is performed, and the "deviation value exceeding the threshold" is recorded as zero or marked as no over-limit. The sum of these calculation and recording results (including the over-limit type and the specific value of the over-limit) forms the "over-limit difference information".

[0104] Based on the "over-limit difference information" generated in the previous step, this information includes the type of deviation exceeding the limit (such as "exceeding the upper limit," "below the lower limit," or "no over-limit") and the specific numerical value exceeding the upper or lower limit of the acceptable range (i.e., the "deviation value exceeding the threshold"). The system performs a final deviation status confirmation. The core of the judgment lies in whether there is a clear, non-zero "deviation value exceeding the threshold" in the "over-limit difference information." Specifically, the system checks the "deviation value exceeding the threshold" recorded in the "over-limit difference information." If the value is greater than zero (indicating that the actual deviation exceeds the upper limit or falls below the meaningful lower limit, and the difference is not zero), then it is determined that there is a valid over-limit value in the "over-limit difference information." Based on this, the system confirms that the current operating status deviation of the sludge conveying pipeline has exceeded the pre-defined deviation. The defined "acceptable deviation range for normal operation" indicates that there may be potential problems with the pipeline operation or that it is already in a non-ideal state. Conversely, if the "deviation value exceeding the threshold" recorded in the "exceedance difference information" is zero, or marked as "no exceedance", it means that there is no valid exceedance value in the "exceedance difference information". Based on this, the system confirms that the current deviation of the sludge conveying pipeline operation does not exceed the "acceptable deviation range for normal operation", indicating that the pipeline operation is within an acceptable normal fluctuation range. Based on this series of judgments and confirmations, the system finally outputs a clear "deviation exceedance status" signal, which directly indicates whether the pipeline operation deviation exceeds the limit. For example, it can be represented by a Boolean value (e.g., yes / no) or presented as a text status (e.g., "deviation exceeds the limit" / "deviation does not exceed the limit").

[0105] The steps for obtaining pipeline flow assessment signals are as follows:

[0106] Based on the deviation exceeding the limit, the current flow capacity level of the sludge conveying pipeline is calculated using the following formula:

[0107]

[0108] Among them, C t F indicates the current flow capacity level of the sludge transport pipeline. i Q represents the flow rate value at the i-th monitoring point. pred,i Let σ represent the predicted flow rate at the i-th monitoring point. i denoted as the standardized volatility (dimensionless) of the i-th monitoring point over the past 30 days, m represents the number of monitoring points judged to have exceeded the deviation limit in the current period, and ∈ is an empirical constant used to prevent the denominator from being zero;

[0109] Based on the current flow capacity level of the sludge conveying pipeline, a continuous interval comparison is performed with the set boundary values ​​of the three flow capacity levels. If the current flow capacity level is greater than the set upper limit threshold, a low flow capacity level flag is output; if it is in the middle range, a normal flow capacity level flag is output; if it is lower than the minimum threshold, it is marked as a high flow capacity level, and a pipeline flow assessment signal is generated.

[0110] Specifically, the formula: The advantage of this formula lies in providing a quantitative method for assessing the overall current flow capacity of sludge transport pipelines. It considers not only the absolute deviation of the flow rate but also the relative magnitude of the deviation and historical fluctuations. By calculating the weighted average relative difference (taking the square root of the absolute value) of the monitoring points determined to have "exceeded the deviation limit," a comprehensive index C can be obtained. t .in, It reflects the relative deviation of the flow rate at the i-th monitoring point, making it comparable between monitoring points with different flow rates. The term, as a weighting factor, implies that for historically large fluctuations in traffic (σ), i The current relative deviation of the monitoring point (which is relatively large) affects the overall C. t The impact of the value will be appropriately reduced; conversely, if a historically stable monitoring point experiences a large deviation, its impact will be more significant. This design makes C... t It can more accurately reflect changes in circulation capacity caused by abnormal factors (rather than inherent historical fluctuations). The final C t It is a dimensionless parameter. The larger the value, the greater the deviation, that is, the lower the flow capacity.

[0111] The steps for obtaining the m parameter are as follows: m represents the total number of monitoring points in the current analysis period that are determined to have exceeded the "acceptable deviation range for normal operation" in the "deviation exceeding the limit status" obtained according to the aforementioned steps. This value is directly derived from the determination results of the "deviation exceeding the limit status". For example, in a certain analysis period, if the "comprehensive operational deviation value" of 3 monitoring points (e.g., monitoring point A, monitoring point C, and monitoring point F) exceeds their corresponding "acceptable deviation range", then m = 3. If no monitoring point has exceeded the deviation limit, then m = 0, and C... t The calculation is meaningless or handled according to special rules (e.g., defined as 0, indicating no deviation and high flow capacity). In this example, there are 2 monitoring points with deviations exceeding the limit, so m = 2.

[0112] F i The steps to obtain the parameters are: F i This represents the actual monitored flow rate of the i-th monitoring point that was determined to have exceeded the deviation limit during the analysis period, typically expressed in cubic meters per second (m³ / s). 3 / s). These flow rates are derived from the "actual monitored flow rate sequence" generated in the previous steps and correspond to the monitoring points included in the deviation calculation in the "monitored predicted flow rate matching pair list". For each out-of-limit monitoring point counted in m, the system extracts its most recent or average actual flow rate value. For example, in the case of m=2, there are two out-of-limit monitoring points, and their corresponding actual monitored flows are: F1 = 0.030m for monitoring point 1. 3 / s, F2 = 0.050m at monitoring point 2. 3 / s.

[0113] Q pred,i The steps to obtain the parameters are: Q pred,i This indicates that the i-th monitoring point, which is judged to have exceeded the deviation limit, is related to F. i The corresponding flow forecast value at the same time point or within the analysis period, in units of F. i Consistent, in cubic meters per second (m 3 / s). These predicted flow rates are derived from the "pipeline parameter prediction baseline" generated in the previous steps and correspond to the predicted values ​​in the "monitoring predicted flow rate matching pair list". For example, for the two over-limit monitoring points mentioned above, their corresponding predicted flows are: Q of monitoring point 1 pred,1 =0.040m 3 / s, Q at monitoring point 2 pred,2 =0.065m 3 / s.

[0114] σ i The steps to obtain the parameter are: σ i Let represent the standardized volatility of the i-th monitoring point that was determined to have exceeded the deviation limit over the past 30 days. It is a dimensionless parameter used to reflect the historical stability of the flow at that monitoring point. The calculation process is as follows: First, collect the daily average flow values ​​of the i-th monitoring point over the past 30 days, denoted as the sequence {q}. i,1 ,q i,2 ,...,q i,30 Next, calculate the arithmetic mean of these 30 daily average flow rates. Then, calculate the standard deviation of these 30 daily average flow values. Finally, the standardized volatility σ i The coefficient of variation is obtained by calculation, and the calculation formula is as follows: For example, for monitoring point 1, if its average daily flow over the past 30 days is 0.042 m³ / s... 3 / s, with a standard deviation of 0.0042m 3 / s, then σ1=0.0042 / 0.042=0.1. For monitoring point 2, if its average daily flow over the past 30 days is 0.060m³ / s... 3 / s, with a standard deviation of 0.009m 3 / s, then σ2=0.009 / 0.060=0.15.

[0115] The steps to obtain the parameter ∈ are as follows: ∈ is an empirical constant, a very small positive value, mainly used to prevent the denominator Q from being affected when calculating the relative difference. pred,i Calculation errors or unstable results may occur due to extreme conditions (such as extremely small or zero predicted flow). The constant should be chosen to be much smaller than the normal predicted flow value, but still effectively avoid division by zero.

[0116] Calculation process: Based on the aforementioned parameter acquisition steps, the following example values ​​are obtained: m = 2. For monitoring point i = 1: F1 = 0.030m 3 / s, Q pred,1 =0.040m 3 / s, σ1=0.1, for monitoring point i=2: F2=0.050m 3 / s, Q pred,2 =0.065m 3 / s, σ²=0.15, empirical constant ∈=0.00001m 3 / s;

[0117] Calculate the inner terms: for i = 1:

[0118]

[0119] For i = 2:

[0120]

[0121]

[0122] Substitute into the formula to calculate C t :

[0123]

[0124] C t ≈0.462522;

[0125] This result indicates that the current sludge transport pipeline has a flow capacity level of C. t The calculated value is 0.462522. This is a dimensionless value that comprehensively reflects the average relative deviation of the monitoring points currently judged to be exceeding the deviation limit, taking into account the stability of historical flow at each point. This value itself does not directly indicate high or low; rather, it needs to be compared with preset level boundary values ​​to determine the specific flow capacity level. The larger the value, the greater the average deviation of the monitored problem points, and the more serious the potential flow capacity problem.

[0126] Based on the "current flow capacity level" C calculated in the previous step... t (For example, the C calculated above) t The system then compares the value (≈0.462522) with the pre-set "three-level flow capacity boundary values" in the system configuration. These boundary values ​​define the division ranges of high, medium, and low flow capacity levels, and are set based on C calculated from the long-term operating history of the pipeline. t The values ​​are statistically analyzed and calibrated in conjunction with actual operational results (e.g., frequency of blockages, conveying efficiency, energy consumption, etc.). For example, by analyzing historical data, two threshold points are set: a low flow capacity threshold T. H and high flow capacity threshold T L These two thresholds will C t The possible range of values ​​for T is divided into three intervals, specifically, T L It can be history C t The 33rd percentile of the value distribution, T H It can be history C t The 66th percentile of the value distribution, or as determined by an experienced pipeline maintenance engineer based on C... t The value is specified by the correspondence between the actual pipeline conditions and the actual pipeline conditions. For example, setting T... L =0.20 and T H =0.50, the comparison logic is as follows: if the calculated current circulation capacity level C t Less than the high flow capacity threshold T L (i.e. C) t If the value is less than 0.20, the system determines the pipeline flow capacity to be at the "high" level; if the current flow capacity level is C... t Greater than or equal to the high flow capacity threshold T L And less than or equal to the low flow capacity threshold T H (i.e., 0.20≤C) t If the value is ≤0.50, it is judged as "normal" level; if the current circulation capacity level is C t Greater than the low flow capacity threshold T H (i.e., C) t If the value is greater than 0.50, it is judged as "low" level, as in the aforementioned example C. t For example, if the value is approximately 0.462522, since 0.20≤0.462522≤0.50, it falls within the normal range. Therefore, the system will output a "normal level" flag. This final level flag is the "pipeline flow assessment signal," which is used to show the overall health status of the pipeline to maintenance personnel.

Claims

1. An online monitoring system for the flow status of sludge conveying pipelines, characterized in that, The system includes: The real-time parameter acquisition module obtains a snapshot of the current system operating parameters based on real-time monitoring of the sludge pump's operating parameters, such as speed, outlet pressure, inlet pressure, and flow and pressure data at the end or middle of the sludge conveying pipeline. The baseline state prediction module generates theoretical hydraulic transport parameters based on the inlet pressure and rotational speed data in the current system operating parameter snapshot and the rheological characteristics of sludge. Combining the theoretical hydraulic transport parameters with the geometric dimension data of the sludge transport pipeline, it calculates the predicted flow rate of the target monitoring point in the pipeline and establishes a pipeline parameter prediction baseline. The operation deviation calculation module compares the flow rate at the end or middle point of the pipeline in the current system operation parameter snapshot with the corresponding predicted value in the pipeline parameter prediction benchmark item by item to obtain a multi-point parameter difference sequence. The multi-point parameter difference sequence is summarized to calculate the value reflecting the degree of inconsistency between the operating state and the predicted state of the sludge conveying pipeline, and the comprehensive operation deviation value is obtained. The pipeline status assessment module compares the comprehensive operational deviation value with the preset acceptable deviation range of the online monitoring system's normal operation status, generates deviation exceeding the limit status, determines the current flow capacity level of the sludge conveying pipeline based on the deviation exceeding the limit status, and obtains the pipeline flow assessment signal. The steps for obtaining the current system operating parameter snapshot are as follows: The speed, outlet pressure, and inlet pressure of the sludge pump, as well as the flow rate and pressure at the end or middle of the sludge conveying pipeline, are collected in real time. The data are recorded synchronously and a unified time stamp is established to obtain a set of real-time monitoring data. Based on the real-time monitoring data set, the sludge pump speed, outlet pressure, and inlet pressure values, as well as the flow rate and pressure values ​​at the end or middle of the sludge conveying pipeline, are paired and combined according to a unified time stamp. Data items at different times caused by sensor response delays are removed to form a snapshot of the current system operating parameters. The steps for obtaining the theoretical hydraulic transport parameters are as follows: Calculate the theoretical hydraulic transport parameters based on the current system operating parameter snapshot; Based on the theoretical hydraulic transport parameters, the theoretical hydraulic transport parameters generated at different time points are summarized and analyzed to identify values ​​with abnormal fluctuations or deviations from the boundary, and unstable samples are removed to form theoretical hydraulic transport parameters. The steps for obtaining the multi-point parameter difference sequence are as follows: Extract the flow rate at the end or middle point of the pipeline from the current system operating parameter snapshot to obtain the actual monitored flow rate sequence; Based on the actual monitored flow sequence, retrieve the predicted flow that matches the corresponding time point and monitoring location in the pipeline parameter prediction benchmark, establish the correspondence between the monitored flow value and the predicted flow value item by item, and generate a list of monitoring and predicted flow matching pairs. Based on the monitoring and predicted flow matching pair list, the difference between the actual monitored flow value and the predicted flow value for each pair is calculated in turn to form a multi-point parameter difference sequence. The steps for obtaining the comprehensive operational deviation value are as follows: The flow difference values ​​of each monitoring point in the multi-point parameter difference sequence are called, all flow differences are arranged according to the time order of the monitoring points, and the minimum, maximum, average and standard deviation of each time period are calculated to generate a parameter difference statistical index group. Based on the parameter difference statistical index group, the maximum difference value, minimum difference value and standard deviation value are extracted, the fluctuation range and central tendency between the values ​​are compared and analyzed, the overall deviation is extracted, and a value reflecting the degree of inconsistency between the operating status and the predicted status of the sludge conveying pipeline is generated. Based on the value reflecting the degree of inconsistency between the operating status and the predicted status of the sludge conveying pipeline, it is determined whether the current difference distribution exceeds the anomaly judgment threshold, and the comprehensive operating deviation value is obtained based on the current difference characteristics.

2. The online monitoring system for the flow status of sludge conveying pipelines according to claim 1, characterized in that, The steps for obtaining the pipeline parameter prediction benchmark are as follows: The theoretical hydraulic transport parameters and the geometric dimensions of the sludge transport pipeline were extracted. The units of the theoretical hydraulic transport parameters and the inner diameter data of the sludge transport pipeline were converted. The pipeline length data was standardized. The three types of data were matched according to the sampling time points to form a unified data set for hydraulic transport and pipeline structure. Based on the unified data set of hydraulic transport and pipeline structure, the predicted flow rate of the target monitoring point in the pipeline is calculated. Based on the predicted flow rate, the predicted flow rate values ​​for all target monitoring points are summarized in chronological order to generate a pipeline parameter prediction baseline.

3. The online monitoring system for the flow status of sludge conveying pipelines according to claim 1, characterized in that, The steps for obtaining the deviation exceeding the limit are as follows: The deviation tolerance range of normal operation is called, the comprehensive operation deviation value is extracted, and the comprehensive operation deviation value is compared with the upper and lower limits of the deviation tolerance range item by item to confirm whether the current comprehensive operation deviation value is within the deviation tolerance range, and a preliminary deviation judgment mark is obtained. Based on the preliminary deviation judgment identifier, calculate the magnitude of the comprehensive operational deviation value that exceeds the upper and lower limits of the deviation acceptance range, record the difference between the upper limit and the lower limit, determine the deviation value that exceeds the threshold, and generate the excess difference information. Based on the above-limit difference information, it is determined whether there is a valid value for the above-limit difference information. If there is a valid value for the above-limit difference information, it is confirmed that the deviation of the sludge conveying pipeline has exceeded the acceptable deviation range. If there is no valid value, it is confirmed that the deviation of the pipeline operation status has not exceeded the limit, and a deviation exceeding the limit status is generated.

4. The online monitoring system for the flow status of sludge conveying pipelines according to claim 1, characterized in that, The steps for obtaining the pipeline flow assessment signal are as follows: Based on the aforementioned deviation exceeding the limit, calculate the current flow capacity level of the sludge conveying pipeline; Based on the current flow capacity level of the sludge conveying pipeline, a continuous interval comparison is performed with the set boundary values ​​of the three flow capacity levels. If the current flow capacity level is greater than the set upper limit threshold, a low flow capacity level flag is output. If it is in the middle range, output the normal level flag; If the flow rate is below the minimum threshold, it is identified as a high flow level, and a pipeline flow assessment signal is generated.

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