Printing and dyeing drainage state analysis method and system based on sensing data

Abstract

The invention relates to the technical field of flood prevention and drainage, and discloses a printing and dyeing drainage state analysis method and system based on sensing data, and the method comprises the steps: obtaining drainage data and generating a curve through deploying sensors which are distributed at equal intervals in groups in a printing and dyeing drainage pipeline; matching the upstream curve by taking the middle / downstream data as a template, and calculating a time difference discrete value to identify sensing abnormality; and according to the upstream data average value, matching an execution instruction, performing hierarchical control on single drainage treatment equipment, multiple drainage treatment equipment in turn or all drainage treatment equipment is started, and performing corresponding pop-up window prompting, early warning or alarming. Meanwhile, environmental interference is corrected through multi-sensor data fusion, database parameters are dynamically optimized to adapt to the production cycle, pipeline undercurrent is recognized in combination with detection particles, and equipment parameters are monitored to early warn faults. According to the method, the printing and dyeing drainage monitoring accuracy and the equipment scheduling intelligence are improved, and the abnormity early warning capability is enhanced.

Description

Technical Field

[0001] The present application relates to the technical field of printing and dyeing drainage, and in particular to a printing and dyeing drainage status analysis method and system based on sensor data. Background Art

[0002] During the printing and dyeing industry's production processes, it produces large amounts of industrial wastewater with complex composition, high chroma, and large fluctuations in water volume. Improper treatment of this wastewater not only causes serious environmental pollution but can also impact production continuity due to problems such as pipe blockages and equipment failures. Therefore, accurate monitoring and efficient control of printing and dyeing wastewater status are key to ensuring that companies meet environmental standards and reduce operational risks. However, current drainage monitoring methods for printing and dyeing companies rely heavily on traditional single-point sensors or manual inspections, which are difficult to adapt to the unique operating conditions of printing and dyeing wastewater and suffer from prominent issues such as insufficient monitoring accuracy and delayed response.

[0003] The limitations of traditional monitoring methods are mainly reflected in three aspects: First, they are susceptible to environmental interference. Dye adhesion, fiber entanglement, pipe corrosion, etc. in printing and dyeing drainage pipes will cause distortion of single-point sensor data, and local fluctuations caused by differences in pipeline structure are often misjudged as real anomalies; second, equipment scheduling lacks flexibility. Drainage treatment equipment mostly operates with fixed parameters and cannot be dynamically adjusted according to drainage status, resulting in insufficient processing capacity during peak periods and energy waste during low periods; third, the ability to identify anomalies is weak. Relying on a single parameter threshold judgment makes it difficult to distinguish between structural interference and real hidden dangers. Hidden problems such as pipeline undercurrents and early equipment failures are difficult to detect in a timely manner.

[0004] Furthermore, the cyclical nature of printing and dyeing production, such as peaks and troughs in batch production and differences in drainage between different processes, makes it difficult for fixed-parameter monitoring models to dynamically adapt to long-term changes. For example, scaling in pipes gradually alters water flow characteristics. If sensor calibration parameters are not updated synchronously, monitoring errors can accumulate, affecting decision-making accuracy. Therefore, developing a printing and dyeing drainage status analysis method that combines precise monitoring, intelligent scheduling, anomaly warnings, and adaptability has become a core requirement for addressing current industry pain points. Summary of the Invention

[0005] In order to monitor the status of printing and dyeing drainage, the present application provides a method and system for analyzing the status of printing and dyeing drainage based on sensor data.

[0006] In a first aspect, the present application provides a method for analyzing the drainage status of printing and dyeing based on sensor data, which adopts the following technical solutions: A method for analyzing the state of printing and dyeing drainage based on sensor data comprises the following steps: Acquiring printing and dyeing drainage data based on preset printing and dyeing drainage sensors, wherein the printing and dyeing drainage sensors are classified into a plurality of groups, wherein each group of printing and dyeing drainage sensors is located at different locations with equal spacing on the same printing and dyeing drainage pipe; periodically recording each group of printing and dyeing drainage data, and processing each group of printing and dyeing drainage data having drainage status information into a printing and dyeing drainage data curve; Using the printing and dyeing drainage data curve corresponding to the midstream or downstream position of the printing and dyeing drainage pipe as a basic template, matching similar intervals with other printing and dyeing drainage data curves, extracting the time intervals of the matched similar intervals, calculating the time difference between the time intervals of the printing and dyeing drainage data curves adjacent to the corresponding positions; and calculating the time discrete values ​​of multiple time differences; If the time discrete value is greater than a preset discrete reference value, a sensor abnormality prompt is issued; otherwise, the content average value of the printing and dyeing drainage data at the upstream position of the printing and dyeing drainage pipe is calculated, and an execution instruction is matched from a preset execution instruction library based on the content average value; In response to the execution instruction, multiple drainage treatment devices are controlled to work: if the printing and dyeing drainage data is within a preset first range, a single drainage treatment device is controlled to work continuously; and a pop-up window prompt is issued; if the printing and dyeing drainage data is within a preset second range, multiple drainage treatment devices are controlled to work in a patrol mode, wherein the number of drainage treatment devices working simultaneously is greater than one; and an early warning prompt is issued; if the printing and dyeing drainage data is within a preset third range, all drainage treatment devices are controlled to work simultaneously, and an alarm prompt is issued; The values ​​in the first range are smaller than the values ​​in the second range, and the values ​​in the second range are smaller than the values ​​in the third range.

[0007] By adopting the above technical solution, sensors are deployed in groups with equal spacing throughout the printing and dyeing drainage pipelines. Real-time drainage data is acquired and processed to generate a curve. Using midstream and downstream data as a template, the upstream curve is matched to calculate discrete time differences. This allows accurate identification of sensor anomalies, such as sensor bias caused by the corrosive nature of printing and dyeing wastewater and monitoring errors caused by pipe scaling. Based on the average value of upstream data, execution instructions are then matched to hierarchical control of drainage treatment equipment, with single units continuously, multiple units in a rotating rotation, or all activated. These controls also provide prompts, warnings, or alarms. This allows for precise monitoring and anomaly diagnosis of flow rates, liquid levels, and other conditions within the printing and dyeing drainage pipelines, avoiding misjudgments of drainage treatment due to sensor failure. By hierarchically controlling drainage treatment equipment, the system adapts to fluctuations in printing and dyeing wastewater discharge, such as differences in water volume during peak and off-peak production periods, improving equipment collaborative processing efficiency and reducing energy consumption. This improves the level of automated decision-making for printing and dyeing wastewater treatment, reduces manual intervention, and allows for rapid response to emergencies such as pipe blockages and sudden increases in water volume, reducing the risk of wastewater overflows.

[0008] Optionally, the step of acquiring printing and dyeing drainage data based on a preset printing and dyeing drainage sensor further includes the following sub-steps: The printing and dyeing drainage sensor is a plurality of liquid level sensors, and the liquid level sensor generates liquid level data; Matching a corresponding fluctuation gain from a preset pipeline fluctuation database according to the position data of the liquid level sensor; Matching a liquid level reference range according to the fluctuation gain; Calculating a plurality of the liquid level height values ​​to obtain a comprehensive liquid level value; If the comprehensive value of the liquid level is outside the preset liquid level reference range, a sensor abnormality prompt is issued; otherwise, the comprehensive value of the liquid level is used as the printing and dyeing drainage data.

[0009] By adopting the above technical solution, for liquid level monitoring in printing and dyeing drainage pipelines, the fluctuation gain is matched from the pipeline fluctuation database based on sensor position. For example, considering the liquid level fluctuation caused by pipeline slope and valve opening and closing, anomalies are identified by comparing the comprehensive liquid level value with the reference range. This eliminates the interference of the topographic difference between the printing and dyeing workshop and the pipeline connection section, the impact of water flow in the pipeline, such as the liquid level fluctuation at the moment of dye vat drainage, on monitoring, ensuring that the liquid level data reflects the actual drainage status. The comprehensive liquid level value is calculated by multi-sensor data fusion to avoid data distortion caused by dye adhesion and corrosion on single-point sensors, and promptly indicate sensor anomalies to ensure the reliability of liquid level monitoring.

[0010] Optionally, the step of acquiring printing and dyeing drainage data based on a preset printing and dyeing drainage sensor further includes the following sub-steps: The printing and dyeing drainage sensor is a plurality of flow rate sensors, and the flow rate sensor generates flow rate data; Matching a corresponding bayonet gain from a preset pipeline bayonet database according to the position data of the flow velocity sensor; Calculating an actual flow rate value according to the flow rate data and the bayonet gain; Calculating a plurality of the actual flow rate values ​​to obtain a flow rate comprehensive value; If the comprehensive flow rate value is outside the preset flow rate reference range, a sensor abnormality prompt is issued; otherwise, the comprehensive flow rate value is used as the printing and dyeing drainage data.

[0011] By adopting the above technical solution, the flow rate monitoring of printing and dyeing drainage is carried out. The sensor position is combined with the bayonet gain from the pipeline bayonet database to match the bayonet gain. For example, the flow rate changes caused by pipe elbows, valves, and other structures are calculated, and the actual flow rate value is integrated into the overall flow rate value. The structural differences of the printing and dyeing drainage pipelines are corrected, such as the bayonet effect where the drainage outlets of different workshops merge into the main pipeline. The influence on flow rate monitoring caused by structural changes is corrected to avoid misinterpreting flow rate changes caused by structural changes as water volume anomalies. The errors caused by fiber impurities entanglement and dye deposition in individual flow rate sensors are filtered out, and the authenticity of the overall flow rate value is ensured through multi-data fusion.

[0012] Optionally, the following sub-steps are also included: Calculating the time when the printing and dyeing drainage data is within the first range as a first time, calculating the time when the printing and dyeing drainage data is within the second range as a second time, and calculating the time when the printing and dyeing drainage data is within the third range as a third time; If the first time is the longest, the ratio of the first time to the second time is calculated as the first time ratio, and the value in the pipeline fluctuation database or the pipeline bayonet database is adjusted in a positive correlation according to the multiplication result of the first time ratio and a preset slow gain value; Otherwise, the ratio of the second time to the third time is calculated as a second time ratio, and the value in the pipeline fluctuation database or the pipeline bayonet database is adjusted inversely according to the multiplication result of the second time ratio and the preset fast gain value.

[0013] By adopting the above technical solution, the parameters of the pipeline fluctuation database or pipeline bayonet database are dynamically adjusted based on the duration of printing and dyeing drainage data in different ranges. When low liquid level / low flow rate conditions persist for a long time, the gain value is adjusted positively to increase sensitivity to subtle changes, avoiding monitoring lags in low-load scenarios caused by fixed database parameters. When the proportion of high liquid level / high flow rate conditions is high, the parameters are adjusted negatively to speed up the response and adapt to scenarios with large and rapidly changing drainage volumes. This achieves self-iterative optimization of database parameters, making the monitoring model more closely aligned with the cyclical drainage characteristics of printing and dyeing production and reducing long-term errors caused by static parameters.

[0014] Optionally, the following sub-steps are also included: Deliver multiple detection particles upstream; A particle sensor is provided downstream, and the particle sensor is capable of measuring and outputting velocity data of the detected particles; sorting the plurality of speed data from largest to smallest; Extracting the velocity data that ranks at the front and reaches a preset percentage, and calculating the average value of the extracted velocity data as a flow velocity comparison value; obtaining the integrated flow velocity value associated with the position of the particle sensor; Calculating a flow velocity difference between the flow velocity comprehensive value and the flow velocity comparison value; If the absolute value of the flow velocity difference is greater than a preset reference absolute value, a pipeline underflow warning prompt is issued.

[0015] By adopting the above technical solution, by deploying detection particles in conjunction with downstream particle sensors, the measured flow velocity of the particles is compared with the combined flow velocity value monitored by the sensor, identifying pipeline undercurrents, such as wastewater leakage caused by pipeline damage and the confluence of unmonitored branch pipes. This overcomes the blind spots of traditional flow velocity sensors in monitoring the complex flow patterns of printing and dyeing drainage pipelines, accurately detecting hidden water flow anomalies. The system sorts and extracts the top particle velocity data, filtering out impurity interference, ensuring that the flow velocity comparison value reflects the actual undercurrent velocity. The difference warning promptly indicates pipeline structural hazards, thus avoiding problems such as wastewater loss and incomplete treatment caused by undercurrents.

[0016] Optionally, the method further comprises the following steps: The detection particles are injection-molded spheres suspended in the printing and dyeing wastewater, and a spherical metal core is provided in the injection-molded spheres. The particle sensor is a metal detector, and the metal detector is used to detect the moving speed of the metal core to obtain the flow rate data. Alternatively, the detection particles are injection-molded spheres suspended in printing and dyeing wastewater, a spherical magnetic core is provided in the injection-molded spheres, the particle sensor is a magnetic field detector, at least two particle sensors are provided, and are separated by a preset detection distance, and the flow rate data is calculated based on the time difference between the two particle sensors detecting the same detection particle and the detection distance.

[0017] By employing the aforementioned technical solution, a detection particle design specifically tailored for dyeing wastewater is constructed. The injection-molded spheres are encased in a metal / magnetic core and paired with a corresponding particle sensor. The injection-molded spheres are resistant to the corrosive effects of dyeing wastewater and feature a regular shape, ensuring stable particle movement with the water flow and accurately reflecting the flow rate. The metal / magnetic core enhances sensor sensitivity, resisting interference from impurities such as dyes and fibers, ensuring reliable velocity data. A dual-magnetic field detector calculates flow rate using time difference, eliminating positional errors associated with a single sensor and further enhancing the accuracy of undercurrent monitoring.

[0018] Optionally, the method further comprises the following steps: Acquiring speed data, voltage data, and current data of the drainage treatment equipment; Calculating a speed falling edge of the rotational speed data, a voltage falling edge of the voltage data, and a current rising edge of the current data; Matching the first time of the speed falling edge, the second time of the voltage falling edge, and the third time of the current rising edge; If the time differences between any two of the first time, the second time, and the third time are all within a preset minimum unit time range, a drainage treatment equipment failure warning prompt is issued.

[0019] By adopting this technical solution, the speed, voltage, and current data of wastewater treatment equipment are monitored, and the temporal correlation between the falling and rising edges is analyzed to provide early warning of equipment failure. Multi-parameter collaborative monitoring can accurately capture early signs of equipment failure, such as mechanical jamming and motor aging. This helps prevent false alarms from single-parameter monitoring, such as voltage fluctuations that may indicate a power grid issue rather than a device failure, thereby improving the accuracy of fault identification. Furthermore, automated warnings are implemented during unmanned periods in the printing and dyeing workshop, facilitating timely equipment maintenance and reducing wastewater treatment interruptions caused by equipment downtime.

[0020] Optionally, the following sub-steps are also included: Calculating a speed component according to the value of the speed falling edge and a preset speed reference value; Calculating a voltage component according to the value of the voltage falling edge and a preset voltage reference value; Calculating a current component according to the value of the current rising edge and a preset current reference value; Calculating the equipment abnormality according to the speed component, voltage component and current component; If the abnormal amount of the equipment is greater than the preset abnormal reference amount, a drainage treatment equipment failure alarm prompt will be issued.

[0021] By adopting this technical solution, the severity of drainage treatment equipment failures can be quantitatively assessed and graded alarms can be issued by calculating the combined anomalies of speed, voltage, and current components. By standardizing multi-parameter changes into equipment anomalies, the system can quantitatively extract fault characteristics and avoid biased judgments caused by differences in absolute parameter values. Setting anomaly reference values ​​enables graded responses, with warnings for minor anomalies and alarms for major failures, reducing unnecessary downtime for maintenance while ensuring that serious failures are addressed promptly. This provides a quantitative basis for equipment maintenance, such as the magnitude of the anomaly reflects the severity of the failure, improving operation and maintenance efficiency and reducing equipment wear.

[0022] Optionally, the method further comprises the following steps: According to the data sorting position of the printing and dyeing drainage data in the first range from small to large, the rotation speed of the single drainage treatment device is positively correlated; the smaller the data sorting position of the printing and dyeing drainage data in the first range, the lower the rotation speed of the drainage treatment device; the larger the data sorting position of the printing and dyeing drainage data in the first range, the higher the rotation speed of the drainage treatment device; According to the data sorting position of the printing and dyeing drainage data in the second range from small to large, the time interval of the drainage treatment device performing the patrol work is adjusted in an anti-correlation manner; the smaller the data sorting position of the printing and dyeing drainage data in the second range, the longer the time interval of the drainage treatment device performing the patrol work; the larger the data sorting position of the printing and dyeing drainage data in the second range, the shorter the time interval of the drainage treatment device performing the patrol work; According to the data sorting position of the printing and dyeing drainage data from small to large within the third range, the total power of all the drainage treatment equipment is adjusted in a positive correlation; the smaller the data sorting position of the printing and dyeing drainage data within the third range, the smaller the total power of all the drainage treatment equipment; the larger the data sorting position of the printing and dyeing drainage data within the third range, the greater the total power of all the drainage treatment equipment.

[0023] By adopting the above technical solution, the speed, patrol interval or total power of the drainage treatment equipment can be dynamically adjusted according to the ranking position of the printing and dyeing drainage data in each range. In low-load scenarios, the speed of a single device is adjusted in a positive correlation with the data ranking to achieve on-demand processing and reduce energy consumption; in medium-load scenarios, the patrol interval is adjusted in a negative correlation. The higher the drainage data, the more frequent the patrol, balancing the load of multiple devices and avoiding dye deposition and increased wear of a single device due to continuous operation; in high-load scenarios, the total power is adjusted in a positive correlation with the data ranking to ensure that the processing capacity matches the drainage volume, such as full power operation during peak production periods to avoid wastewater backlog; the equipment scheduling is refined and adaptive, taking into account processing efficiency, energy consumption control and equipment life, and adapting to the dynamic drainage needs of printing and dyeing production.

[0024] In a second aspect, the present application provides a printing and dyeing drainage status analysis system based on sensor data, which adopts the following technical solutions: A printing and dyeing drainage state analysis system based on sensor data comprises a processor, wherein the processor executes the steps of any one of the above-mentioned printing and dyeing drainage state analysis methods based on sensor data.

[0025] In summary, this application includes at least one of the following beneficial technical effects: By deploying sensors in groups with equal spacing, implementing multi-data fusion correction and abnormal sensor identification mechanisms, we can effectively filter out monitoring errors caused by factors unique to printing and dyeing wastewater, such as dye adhesion, fiber interference, and pipeline corrosion, to ensure that data such as liquid level and flow rate truly reflect the drainage status.

[0026] Based on the hierarchical response mechanism of drainage data, single-unit continuous, multiple-unit patrol, and full-start operation are carried out, and the equipment speed, patrol interval and total power are dynamically adjusted in combination with data sorting. This can not only adapt to the periodic drainage fluctuations of printing and dyeing production, such as the difference in water volume during peak / valley periods, but also balance energy consumption and processing efficiency, reduce equipment no-load or overload, and extend equipment life.

[0027] Through dynamic iterative optimization of database parameters, undercurrent identification, and multi-parameter collaborative diagnosis of equipment failures, the system can autonomously adapt to the complex scenarios of printing and dyeing drainage, accurately warn of pipeline structure hazards, equipment failures, and sensor anomalies, reduce manual intervention costs, and improve emergency response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a step diagram of a method for analyzing the drainage status of printing and dyeing based on sensor data.

[0029] Figure 2 The printing and dyeing drainage sensor is a plurality of liquid level sensors, and a step diagram of obtaining printing and dyeing drainage data based on a preset printing and dyeing drainage sensor.

[0030] Figure 3 The printing and dyeing drainage sensor is a plurality of flow rate sensors, and a step diagram of obtaining printing and dyeing drainage data based on a preset printing and dyeing drainage sensor. DETAILED DESCRIPTION

[0031] Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings.

[0032] Throughout this specification, reference to the terms "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0033] The present application discloses a method for analyzing the drainage status of printing and dyeing based on sensor data, referring to Figure 1 , including the following steps: Pre-installed drainage sensors were deployed throughout the main and branch drainage pipes within the dyeing and printing plant. These sensors include liquid level sensors, flow rate sensors, and water quality sensors, including pH sensors and turbidity sensors. These sensors were divided into three groups based on the pipeline's direction: an upstream group near the dye vat outlets, a midstream group in the middle of the pipeline, and a downstream group near the sewage treatment plant entrance. Each group of sensors was evenly spaced 5-8 meters along the pipeline to ensure coverage of key nodes.

[0034] Data from each sensor group is collected periodically (e.g., every 10 seconds) via the industrial bus and cleaned. Outliers that clearly exceed the physical range, such as negative values ​​displayed by the liquid level sensor, are removed. Missing values ​​due to signal interruptions are filled in, and the average value of the five preceding and following cycles is used for interpolation. Effective information reflecting drainage status (such as liquid level height and water flow velocity) in each data set is processed into a time-value curve. For example, the upstream group liquid level curve uses time as the horizontal axis and liquid level height (in meters) as the vertical axis, visually displaying the changing trends of drainage status.

[0035] The printing and dyeing drainage data curve for either the midstream or downstream group is selected as the base template, with the downstream group being preferred as it is less susceptible to local disturbances. The Dynamic Time Warping (DTW) algorithm is then used to match similar intervals with the curves of the upstream group and other branch pipes. For example, if the downstream group experiences a rising liquid level between 8:00 and 8:10, the DTW algorithm is used to match the upstream group with the same trend, such as between 7:58 and 7:68. The time difference between the similar intervals of the two curves is then extracted, which is 2 minutes in this case.

[0036] Calculate the time difference of all adjacent position curves, such as the upstream group and the midstream group, and the midstream group and the downstream group. Use the standard deviation formula to calculate the discrete values ​​of multiple time differences: if the time differences of 5 groups are 120 seconds, 130 seconds, 110 seconds, 140 seconds, and 125 seconds respectively, the standard deviation is about 10.8 seconds, which is the time discrete value.

[0037] The preset discrete reference value is set according to the pipeline length, such as 15 seconds. If the calculated time discrete value (10.8 seconds) is less than the reference value, the sensor is judged to be normal. If a group of sensors causes data delay due to dye adhesion, the time discrete value increases to 20 seconds, the system triggers a sensor abnormality prompt, and the monitoring terminal displays "Upstream group No. 2 sensor is abnormal, please check."

[0038] When the sensor is normal, calculate the average value of all valid data in the upstream group, such as the average liquid level = (h1+h2+h3) / 3, where h1-h3 is the liquid level value of the three sensors in the upstream group. The preset execution instruction library contains three levels of instructions: The first instruction corresponds to the first range, liquid level < 0.3 meters, flow rate < 0.5m / s; The second instruction corresponds to the second range, 0.3 meters ≤ liquid level < 0.6 meters, 0.5 m / s ≤ flow rate < 1.0 m / s; The third instruction corresponds to the third range, liquid level ≥ 0.6 meters, flow rate ≥ 1.0m / s.

[0039] The instruction is matched according to the average value of the upstream group. For example, if the average value shows a liquid level of 0.4 meters and a flow rate of 0.7 m / s, the second instruction is matched.

[0040] Respond to matching execution instructions and control multiple drainage treatment equipment (such as screen machines, lift pumps, and equalization tank agitators): If the first instruction (low load state) is triggered, one lift pump is controlled to operate continuously, and the speed is set to 60% of the rated speed. A pop-up window will appear on the monitoring terminal prompting "The current drainage load is low and the equipment is operating normally"; If the second instruction (medium load state) is triggered, the three lifting pumps are controlled to work in rotation, with each pump working for 10 minutes and switching every time. Two pumps are running at the same time, the high-frequency operation (50Hz) of the grille machine is started, and an early warning prompt is issued through the sound and light alarm (yellow light flashes); If the third instruction (high load state) is triggered, all five lifting pumps will be controlled to run at full power, the emergency regulating tank inlet valve will be opened, and the monitoring terminal will issue an alarm prompt with a red light and a buzzer, and a text message will be pushed to the operation and maintenance personnel's mobile phone.

[0041] Through the above steps, closed-loop control of printing and dyeing wastewater from monitoring to treatment is achieved, which allows for rapid response to high-load drainage during peak production periods (such as 9:00-12:00), optimizes equipment energy consumption during low-load periods at night, and improves the stability and economy of printing and dyeing wastewater treatment.

[0042] Reference Figure 2 The step of obtaining the printing and dyeing drainage data based on the preset printing and dyeing drainage sensor further includes the following sub-steps: Multiple liquid level sensors are deployed at key locations along the dyeing and printing wastewater pipelines, such as one meter downstream of the dye vat outlet, at points where the pipe slope changes, and within three meters before and after valves. The sensor housings are constructed of 316L stainless steel to resist the corrosive effects of dyeing and printing wastewater. The sensors generate real-time liquid level data (in meters). For example, when a dye vat is draining, the sensors can detect level fluctuations of 0.1-0.8 meters and transmit this data to the central control system via a 4G / NB-IoT module.

[0043] A preset pipeline fluctuation database stores fluctuation characteristic parameters at different locations: based on pipeline slope (such as 0.5°, 1°, 2°), valve type (gate valve, ball valve), proximity to the drain outlet, etc., corresponding fluctuation gains (ranging from 0.8 to 1.2) are generated in advance. For example: The sensor close to the dye vat drain outlet is greatly impacted by instantaneous drainage, and the matching fluctuation gain is 1.2; In the gentle section of the pipeline, the sensor with a slope of 0.5° is matched with a fluctuation gain of 1.0; The sensor within 5 meters behind the valve is disturbed by the valve opening and closing, and the matching fluctuation gain is 1.1.

[0044] The central control system automatically matches the corresponding fluctuation gain from the pipeline fluctuation database based on the GPS position data of the liquid level sensor (error ≤ 0.5 meters).

[0045] Calculate the liquid level reference range based on the fluctuation gain: take the historical average liquid level at that location (the average value of the same period in the past 30 days) as the reference value, and the reference range is [reference value × (1-fluctuation gain × 0.1), reference value × (1+fluctuation gain × 0.1)]. For example: The historical average liquid level of a sensor is 0.3 meters, and the matching fluctuation gain is 1.2. The reference range is [0.3×(1-1.2×0.1), 0.3×(1+1.2×0.1)]=[0.264 meters, 0.336 meters].

[0046] A weighted average method is used to calculate the combined value of multiple liquid level sensors: sensors near key nodes (such as drains and valves) are assigned higher weights (0.2-0.3), while sensors in the middle of the pipeline are assigned lower weights (0.1-0.15), with the total weight being 1. For example, if the liquid level values ​​of three sensors are 0.28 meters (weight 0.3), 0.30 meters (weight 0.4), and 0.32 meters (weight 0.3), respectively, the combined liquid level value is 0.28 × 0.3 + 0.30 × 0.4 + 0.32 × 0.3 = 0.30 meters.

[0047] If the comprehensive value of the liquid level (0.30 meters) is within the reference range (0.264-0.336 meters), it will be used as valid printing and dyeing drainage data; if the comprehensive value is 0.35 meters (exceeding the upper limit) at a certain moment, the central control system will immediately issue a sensor abnormality prompt; if the group of sensors is marked red on the monitoring interface, a text message will be pushed to the operation and maintenance personnel: "The comprehensive value of the liquid level of the downstream group is abnormal, suspected sensor failure or pipeline blockage."

[0048] Through this embodiment, the liquid level monitoring error caused by terrain and equipment disturbance in the printing and dyeing drainage pipeline can be effectively eliminated, ensuring that the data truly reflects the drainage status and providing a reliable basis for subsequent drainage treatment equipment scheduling.

[0049] Reference Figure 3 The step of obtaining the printing and dyeing drainage data based on the preset printing and dyeing drainage sensor further includes the following sub-steps: Multiple electromagnetic flow velocity sensors are deployed at key structural nodes (such as elbows, reducers, and the confluence of workshop drain outlets) and straight sections of the printing and dyeing drainage pipeline. The sensor probes are made of polytetrafluoroethylene to prevent dye adhesion and fiber entanglement. The sensors are installed along the pipeline axis and inserted to a depth of 1 / 3 of the pipeline diameter to ensure that the probes are fully immersed in the wastewater and not interfered with by the pipeline wall.

[0050] The flow rate sensor generates raw flow rate data (unit: m / s) at a frequency of 5 seconds. For example, at a pipe elbow, the sensor may collect fluctuating values ​​such as 1.2m / s and 1.3m / s. The data is transmitted in real time to the data processing terminal via industrial Ethernet.

[0051] A preset pipeline bayonet database is built based on pipeline CAD drawings and field surveying, storing the structural parameters of each position and the corresponding bayonet gain: The straight section of the pipeline has no structural mutation, the bayonet gain is 1.0, and no correction is required; At the 90° elbow, the flow velocity changes suddenly due to the turning direction. The bayonet gain is 0.85, and the flow velocity measurement value is usually too high and needs to be corrected downward. For example, when the pipe diameter is reduced from DN300 to DN200, the bayonet gain is 0.7. The flow velocity is artificially high due to the reduction in cross-section, and the correction range is larger. At the confluence of multiple workshop drainage outlets, the water flow disturbance is severe, the bayonet gain is 0.9, and local turbulence causes fluctuations in the measurement value, which requires slight correction.

[0052] The data processing terminal matches the sensor's location information with its installation coordinates (accurate to 0.1 meter) and retrieves the corresponding bayonet gain from the bayonet database. For example, a sensor located 2 meters downstream of the DN300 to DN200 reducer is matched with a bayonet gain of 0.7.

[0053] Use the formula "actual flow rate value = original flow rate data × bayonet gain" for correction. For example: The original flow velocity collected by the variable diameter section sensor is 1.5 m / s, and the bayonet gain is 0.7. The actual flow velocity value = 1.5 × 0.7 = 1.05 m / s; The original flow velocity of the straight section sensor is 0.8 m / s, and the bayonet gain is 1.0, so the actual flow velocity value = 0.8 × 1.0 = 0.8 m / s.

[0054] The corrected data can eliminate systematic errors caused by the pipeline structure. For example, the false high flow rate at the elbow is corrected to the true value, avoiding misjudgment as a sudden increase in water volume.

[0055] Actual velocity values ​​from six sensors along the pipeline were selected and the median average method was used to calculate the composite velocity value. The median average was then removed, excluding the maximum and minimum values ​​to reduce interference from extreme values. For example, at a given moment, the six actual velocity values ​​were 0.9 m / s, 1.05 m / s, 0.8 m / s, 1.2 m / s, 0.95 m / s, and 1.1 m / s. After removing 0.8 m / s and 1.2 m / s, the composite value was (0.9 + 1.05 + 0.95 + 1.1) ÷ 4 = 1.0 m / s.

[0056] The preset flow rate reference range is set according to the designed flow rate of the pipeline, such as 0.5-1.5m / s. If the comprehensive flow rate value (1.0m / s) is within the range, it will be used as effective printing and dyeing drainage data; if the comprehensive value drops to 0.3m / s (below the lower limit) at a certain moment due to the sensor being entangled by fibers, the system will immediately trigger a sensor abnormality prompt, and the monitoring terminal will display "The comprehensive flow rate value of the midstream section is abnormal, it is recommended to check the entanglement of the sensor probe", and automatically mark this group of data as pending verification.

[0057] Through this embodiment, the interference of the pipeline structure on flow rate monitoring can be accurately corrected to ensure that the data reflects the actual water flow state, providing a reliable basis for predicting pipeline blockage (sudden drop in flow rate) and adjusting the load of drainage treatment equipment.

[0058] This embodiment optimizes the parameters of the pipeline fluctuation database and the pipeline bayonet database by analyzing the duration of printing and dyeing drainage data in different ranges to achieve self-iterative adjustment of the gain value. The specific process is as follows: The system automatically counts the distribution time of printing and dyeing drainage data (comprehensive liquid level value or comprehensive flow rate value) in the past 24 hours at 00:00 every morning: First time (t1): The cumulative duration of the data in the first range (liquid level < 0.3 m or flow rate < 0.5 m / s), for example, 10 hours during the non-production period (22:00-8:00 the next day); Second time (t2): The cumulative duration of the data within the second range (0.3 m ≤ liquid level < 0.6 m or 0.5 m / s ≤ flow velocity < 1.0 m / s), for example, the 2 hours of the morning shift preparation phase (8:00-10:00); Third time (t3): The cumulative duration that the data is in the third range (liquid level ≥ 0.6 m or flow rate ≥ 1.0 m / s), for example, 8 hours during the production peak (10:00-18:00).

[0059] By comparing the sizes of t1, t2, and t3, we can determine the current data distribution characteristics: if t1 (10 hours) is the longest, it belongs to the low-load-dominated scenario; otherwise (if t3 is the longest), it belongs to the medium-to-high-load-dominated scenario.

[0060] When t1 is the longest, calculate the first time ratio k1 = t1 / t2 (e.g., 10 / 2 = 5). Preset the slow gain value g1 = 0.02, and calculate the adjustment coefficient Δ1 = k1 × g1 = 5 × 0.02 = 0.1.

[0061] To make positive adjustments to the gain values ​​in the pipeline fluctuation database or pipeline bayonet database: The original fluctuation gain of the flat section sensor is 1.0, and after adjustment it is 1.0×(1+Δ1)=1.1; The original bayonet gain of the sensor at the elbow was 0.85, and after adjustment it became 0.85×(1+Δ1)=0.935.

[0062] After adjustment, the database is more sensitive to subtle changes in low-range data (such as the liquid level rising from 0.25 meters to 0.28 meters), avoiding the neglect of small anomalies during non-production periods.

[0063] When t1 is not the longest (e.g., t3 is the longest), calculate the second time ratio k2 = t2 / t3 (e.g., 2 / 8 = 0.25). Preset the fast gain value g2 = 0.04, and calculate the adjustment coefficient Δ2 = k2 × g2 = 0.25 × 0.04 = 0.01.

[0064] To adjust the gain value in the database in reverse order: The original fluctuation gain of the drain outlet sensor is 1.2, and after adjustment it is 1.2×(1-Δ2)=1.188; The original bayonet gain of the variable diameter sensor is 0.7, and after adjustment it becomes 0.7×(1-Δ2)=0.693.

[0065] After adjustment, the database parameters respond faster to mid- and high-range data. For example, when the flow rate suddenly increases from 0.9m / s to 1.1m / s during peak production, the system can quickly identify and trigger corresponding processing instructions.

[0066] To prevent excessive parameter drift, upper and lower limits are set for gain adjustment: 0.8-1.5 for fluctuating gain and 0.6-1.2 for bayonet gain. If the limits are exceeded, the boundary value is automatically applied. After each adjustment, the system generates a log documenting the adjustment time, original gain value, adjustment coefficient, and new value. For example, "2023-10-01 00:00, bayonet gain of the midstream elbow sensor was adjusted from 0.85 to 0.935, with an adjustment coefficient of 0.1 (due to k1=5)."

[0067] Through this embodiment, the database parameters can dynamically adapt to the periodic fluctuations of printing and dyeing production, improve monitoring sensitivity at low loads, speed up response speed at high loads, and reduce parameter solidification errors in long-term operation.

[0068] This embodiment uses detection particles in conjunction with sensors to accurately identify undercurrents in printing and dyeing drainage pipes, addressing the blind spots of traditional flow rate monitoring. The specific process is as follows: Injection-molded spheres with a diameter of 5-8mm are selected as detection particles. The density of the spheres is close to that of printing and dyeing wastewater (1.02-1.05g / cm³), ensuring that they are suspended in water and move with the water flow. The surface of the spheres is sprayed with a corrosion-resistant coating, and a 2mm diameter neodymium iron boron magnetic core (remanent magnetic strength ≥1200mT) is encapsulated inside to improve detection sensitivity.

[0069] Every day, an automatic delivery device is installed upstream of the printing and dyeing drainage main pipeline and 3 meters downstream of the workshop drainage outlet. The test particles are delivered at regular intervals (such as 9:00 and 15:00). The number of test particles delivered at a time is 20-30, which is adjusted according to the pipe diameter. 25 particles are delivered for a DN300 pipe. The particle delivery interval is 5 seconds per particle to avoid collisions that affect the movement trajectory.

[0070] Two magnetic field detectors, i.e. particle sensors, are installed on the outer wall of the pipeline 50 meters downstream of the delivery point, at intervals of 3 meters along the direction of water flow. The detectors use a Hall element array and can identify changes in the magnetic field when magnetic particles pass through. The sampling frequency is 1kHz and the positioning accuracy is ±0.05 meters.

[0071] When a particle passes through the first detector, the system records the timestamp t1; when it passes through the second detector, the system records the timestamp t2. The velocity of a single particle (in m / s) is calculated using the formula "particle velocity = 3 m / (t2 - t1)." For example, if the time difference between a particle passing through the two detectors is 4.5 seconds, its velocity = 3 / 4.5 ≈ 0.67 m / s.

[0072] The system collects velocity data for all detected particles. For example, 25 particles generate 25 sets of velocity values, which are sorted from largest to smallest: assuming the data is 0.82m / s, 0.79m / s, 0.75m / s, 0.67m / s...0.31m / s. Some particles have lower velocity due to attached fibers.

[0073] The default percentage is 30%, which means extracting the first 30% of high-velocity data. For 25 data points, extract the first 8, 25 × 30% = 7.5, rounded up to 8. The average is calculated as the velocity comparison value: (0.82 + 0.79 + 0.75 + 0.73 + 0.71 + 0.69 + 0.68 + 0.67) ÷ 8 ≈ 0.72 m / s. This value can filter out low-velocity anomalies caused by interference from particles and impurities, reflecting the true water flow rate.

[0074] Retrieve the comprehensive flow velocity value associated with the downstream particle sensor position, which is the data monitored and corrected by the flow velocity sensor, such as 0.55 m / s. Calculate the flow velocity difference = comprehensive flow velocity value - flow velocity comparison value = 0.55-0.72 = -0.17 m / s, and its absolute value is 0.17 m / s.

[0075] The preset reference absolute value is 0.1m / s, which is set according to the designed flow velocity fluctuation range of the pipeline. Since 0.17m / s>0.1m / s, the system determines that there is an undercurrent. If the pipeline is partially damaged, resulting in partial water leakage, the sensor monitoring value is lower than the actual flow velocity), and the pipeline undercurrent warning prompt is immediately triggered: the monitoring terminal displays "Abnormal flow velocity difference detected 50 meters downstream, suspected pipeline undercurrent, it is recommended to check the pipeline integrity." At the same time, the warning location is marked on the pipeline electronic map, and the maintenance work order is pushed to the operation and maintenance system.

[0076] Through this embodiment, hidden undercurrents caused by corrosion perforation, loose joints, etc. in printing and dyeing drainage pipes can be effectively identified, avoiding environmental risks and water resource waste caused by wastewater leakage. It is especially suitable for pipeline safety monitoring in old factory areas.

[0077] In this embodiment, two adaptation schemes of detection particles and sensors are designed for the special environment of printing and dyeing wastewater to ensure the accuracy and anti-interference of flow velocity measurement, as follows: Option 1: Combination of Metal Core Detection Particles and Metal Detectors The test particles are 6mm diameter polypropylene injection-molded spheres (density 1.03g / cm³). The spheres are treated with a 0.2mm thick polytetrafluoroethylene coating, which is corrosion-resistant to dyeing wastewater with a pH range of 3-11. Their smooth surface resists the adhesion of dyes and fibers. A 3mm diameter 304 stainless steel core (containing an iron-nickel alloy) is encapsulated within the spheres. The core is concentric with the sphere to ensure a stable center of gravity, preventing tumbling and deflection with the water flow.

[0078] The particle sensor uses a high-frequency metal detector (operating at 100kHz) installed on the outer wall of a printing and dyeing drainage pipe, one-third the pipe diameter from the bottom. The distance between the detector probe and the pipe wall is ≤5mm, and the detection range is a circular area with a diameter of 150mm. When a metal core particle flows through the detection area, the detector outputs a pulse signal and records the time the signal is triggered. Using two metal detectors spaced 5 meters apart along the flow direction, the time difference Δt between the passages of the same particle is calculated. For example, if the particle passes through the first detector at time t1 and the second at time t2, Δt = t2 - t1, and the flow velocity is calculated as 5m / Δt. For example, if Δt = 8 seconds, the flow velocity is 5 / 8 = 0.625m / s.

[0079] This solution is suitable for scenarios where the pipe diameter is ≤500mm. The metal core has strong anti-interference ability against dyes and auxiliaries, and is particularly suitable for flow rate measurement of high-chroma printing and dyeing wastewater.

[0080] Option 2: Combination of magnetic core detection particles and magnetic field detectors The test particles are 8mm diameter polyethylene injection-molded spheres (density 1.04g / cm³) with a 1mm thick wall. They contain a 4mm diameter NdFeB permanent magnet core with a magnetic strength of 800-1000 gauss. The core is coated with an epoxy resin insulation layer to prevent electrochemical reactions with chloride ions in printing and dyeing wastewater. The spheres are streamlined (with a 2mm radius at each end) to reduce flow resistance and ensure a flow error of ≤5%.

[0081] The particle sensor uses a dual-channel magnetic field detector. Each detector contains three Hall elements arranged in an equilateral triangle, which can locate the passing position of magnetic particles. Two magnetic field detectors are installed 4 meters apart along the pipeline axis, with a preset detection distance of 4 meters. When the same magnetic particle passes through the first detector, the system records time t3; when it passes through the second detector, time t4 is recorded. The time difference Δt' is calculated as t4-t3, and the flow velocity data is calculated as 4m / Δt'. For example, if Δt' = 6 seconds, the flow velocity is 4 / 6, which is ≈ 0.667m / s.

[0082] To avoid signal interference from adjacent particles, the interval for testing particle delivery is set to 10 seconds per particle, and the number of particles delivered at a single time shall not exceed 1 / 5 of the ratio of the pipe cross-sectional area to the particle cross-sectional area; for example, the number of particles delivered at a single time in a DN600 pipe shall be ≤15.

[0083] Option 1 is suitable for printing and dyeing wastewater with a large amount of magnetic impurities, such as denim dyeing and finishing wastewater, and background interference can be filtered out by adjusting the detector frequency; Option 2 is suitable for high-flow pipelines (≥1.0m / s), and the time difference calculation of dual detectors can eliminate the response delay error of a single sensor; the injection-molded spheres of both options are corrosion-resistant and have a density close to that of printing and dyeing wastewater, ensuring that the particle movement state is consistent with the water flow, providing a reliable speed benchmark for undercurrent identification.

[0084] Through this embodiment, the combination of detection particles and sensors can adapt to the complex composition and flow state of printing and dyeing wastewater, provide accurate data support for the calculation of flow velocity comparison values, and improve the reliability of pipeline undercurrent monitoring.

[0085] This embodiment targets key equipment in the printing and dyeing wastewater treatment process, such as lift pumps, screen machines, and agitators. It uses multi-parameter collaborative monitoring to achieve early warning of faults and avoid drainage system paralysis caused by sudden equipment shutdowns. The specific process is as follows: Install the sensor module in the motor control box of the drainage treatment equipment, including: Hall speed sensor: installed at the motor output shaft end, with a sampling frequency of 100Hz, real-time acquisition of equipment speed data (unit: r / min), such as the normal speed of the boost pump is 1450r / min; Voltage sensor: connected in series to the motor power supply circuit, with a measurement range of 0-380V, and collects the average value of the three-phase voltage (unit: V); Current sensor: connected to the circuit through a current transformer, with a measurement range of 0-50A, and collects the average value of the three-phase current (unit: A).

[0086] Sensor data is transmitted to the equipment monitoring system via the industrial bus, with a storage cycle of 1 second per item to ensure that instantaneous parameter changes are captured.

[0087] The monitoring system performs trend analysis on the collected speed, voltage, and current data and extracts key features along the following lines: Speed ​​falling edge: refers to the starting moment when the speed data starts to drop continuously from a stable value. For example, if the speed of the lift pump drops suddenly from 1450r / min to 1300r / min, the first time of the falling edge is recorded as t1, such as 10:05:23.120; Voltage falling edge: refers to the time when the voltage data deviates from the rated value (such as 380V) and continues to decrease. For example, when the voltage drops from 380V to 360V, the second time of the falling edge is recorded as t2, such as 10:05:23.125; Current rising edge: refers to the starting moment when the current data begins to abnormally increase from the normal range (such as 15A). For example, when the current rises from 15A to 20A, the third time of the rising edge is recorded as t3, such as 10:05:23.130.

[0088] The judgment of the characteristic edge must meet the requirement of "consistent data change trend for three consecutive cycles (3 seconds)" to avoid misjudgment due to instantaneous interference.

[0089] Calculate the time difference between three characteristic edges: Δt1=|t2-t1| (the time difference between the voltage falling edge and the speed falling edge); Δt2=|t3-t1| (the time difference between the rising edge of current and the falling edge of speed); Δt3=|t3-t2| (the time difference between the rising edge of current and the falling edge of voltage).

[0090] The preset minimum unit time range is 0-0.5 seconds, which is set according to the device response delay characteristics. If the above three time differences all satisfy Δt≤0.5 seconds (for example, Δt1=0.005 seconds, Δt2=0.010 seconds, and Δt3=0.005 seconds), it is determined that the abnormal changes in the three parameters are strongly correlated, indicating that the device is at risk of mechanical or electrical failure.

[0091] When the time correlation conditions are met, the monitoring system immediately executes: A warning window pops up on the equipment operation and maintenance interface, displaying "Boost pump 1# detected a simultaneous decrease in speed, voltage, and current, suggesting mechanical obstruction. Please check immediately." Push warning SMS messages to the operation and maintenance personnel's mobile phones, including the device number, abnormal parameter value and timestamp; Automatically record the parameter curve 10 minutes before the fault as a basis for fault diagnosis.

[0092] If only a single parameter is abnormal, such as voltage drops but speed and current are normal, it is judged to be external interference such as power grid fluctuations. No warning is triggered and only the abnormality log is recorded.

[0093] This embodiment uses multi-parameter time correlation analysis to accurately identify early equipment failures, such as jamming caused by bearing wear and aging of motor windings. Compared with traditional single-parameter alarms, it reduces the false alarm rate and ensures the stable operation of printing and dyeing wastewater treatment equipment during unmanned periods.

[0094] Based on fault warning, this embodiment achieves accurate assessment of fault severity by quantifying abnormalities in computing equipment, providing a basis for hierarchical processing. The specific process is as follows: Extract drainage treatment equipment, such as the speed falling edge value, voltage falling edge value, and current rising edge value of the printing and dyeing wastewater lifting pump: Speed ​​falling edge value: refers to the change in speed from the stable value to the falling edge moment, that is, Δv = rated speed - speed at the falling edge moment (unit: r / min). For example, if the rated speed is 1450 r / min and the speed at the falling edge moment is 1300 r / min, then Δv = 150 r / min. Voltage falling edge value: refers to the change in voltage from the rated value to the falling edge moment, that is, Δu = rated voltage - voltage at the falling edge moment (unit: V). For example, if the rated voltage is 380V and the voltage at the falling edge moment is 350V, then Δu = 30V; Current rising edge value: refers to the change in current from the normal peak value to the rising edge moment, that is, Δi = current at the rising edge moment - normal peak current (unit: A). For example, if the normal peak current is 18A and the current at the rising edge moment is 25A, then Δi = 7A.

[0095] Preset reference values ​​(based on equipment factory parameters and historical operating data): Speed ​​reference value v0 = 200r / min (maximum allowable speed drop); Voltage reference value u0=50V (maximum allowable voltage drop); The current reference value i0=10A (the maximum current rise allowed).

[0096] The standardized formula is used to calculate the parameters and normalize the changes in different dimensions: Velocity component: Sv=Δv / v0, for example 150 / 200=0.75; Voltage component: Su = Δu / u0, for example 30 / 50 = 0.6; Current component: Si = Δi / i0, for example 7 / 10 = 0.7.

[0097] The component value range is 0-1, and the closer it is to 1, the more significant the deviation of the parameter from the normal state.

[0098] The weighted summation formula is used to calculate the equipment abnormality A: A=0.4×Sv+0.3×Su+0.3×Si. The weight is set according to the degree of influence of the parameter on the equipment failure. The speed drop is more sensitive to mechanical failure, so it has the highest weight.

[0099] For example: A=0.4×0.75+0.3×0.6+0.3×0.7=0.3+0.18+0.21=0.69.

[0100] The preset abnormal reference value A0=0.8 is calibrated according to the critical failure data of the equipment: If A≤0.8 (such as 0.69 above), it is judged as a minor abnormality, and the fault warning state is maintained, with a prompt "There is a potential fault in the equipment, and it is recommended to repair it within 24 hours"; If A > 0.8, a fault alarm is triggered. For example, when Δv = 180 r / min, Δu = 40 V, Δi = 9 A, Sv = 0.9, Su = 0.8, Si = 0.9, A = 0.4 × 0.9 + 0.3 × 0.8 + 0.3 × 0.9 = 0.36 + 0.24 + 0.27 = 0.87 > 0.8, the system immediately executes: Issue an audible and visual alarm (red warning light flashes + buzzer sounds); Automatically switch the equipment to the backup pump operation (if it is a redundant system); Generate an emergency work order on the operation and maintenance platform, marking the abnormal value and the proportion of each component, such as "the current component accounts for 31%, suspected motor overload."

[0101] Reference values ​​are updated monthly based on equipment maintenance records. For example, if the actual allowable speed drop for a particular batch of lift pumps is 180 rpm due to impeller wear, V0 will be adjusted to 180 rpm to ensure that the component calculations are consistent with the aging status of the equipment. Furthermore, for equipment with an abnormality exceeding 0.6, the monitoring frequency will be increased from 1 second to 0.5 seconds to capture subtle changes.

[0102] Through this embodiment, equipment failures can be upgraded from "qualitative warning" to "quantitative assessment". Operation and maintenance personnel can prioritize serious faults based on the size of the abnormality (for example, equipment with A=0.95 takes precedence over equipment with A=0.7), reducing unnecessary downtime. At the same time, data support is provided for fault location. For example, if the Si ratio of a certain device reaches 40%, the motor winding can be the focus of the investigation.

[0103] This embodiment implements fine-tuning of drainage treatment equipment under different load scenarios by sorting the printing and dyeing drainage data, balancing treatment efficiency and energy consumption costs. The specific process is as follows: The system collects printing and dyeing drainage data (comprehensive value of liquid level or comprehensive value of flow rate) and divides it into intervals on a daily basis: The first range: 0.1-0.3 meters (liquid level) or 0.2-0.5m / s (flow rate), sorted from small to large into 10 gears (positions 1 to 10), for example, position 1 corresponds to 0.1 meters, and position 10 corresponds to 0.3 meters; The second range: 0.3-0.6 meters (liquid level) or 0.5-1.0m / s (flow rate), also sorted into 10 gears (positions 1 to 10), position 1 corresponds to 0.3 meters, position 10 corresponds to 0.6 meters; The third range: 0.6-1.0 meters (liquid level) or 1.0-1.5m / s (flow rate), sorted into 10 gears (positions 1 to 10), position 1 corresponds to 0.6 meters, and position 10 corresponds to 1.0 meters.

[0104] Each gear position corresponds to a specific numerical threshold, for example, the first range position 5 corresponds to 0.2 meters ((0.3-0.1)×5 / 10+0.1).

[0105] When the printing and dyeing drainage data is in the first range, the speed of a single drainage treatment device (such as a stirrer in an equalization tank) is adjusted in a positive correlation with the sorting position: The preset speed adjustment range is 300-600r / min (30%-60% of the rated speed of the equipment); Adjustment formula: actual speed = 300 + (sort position - 1) × 33.3 (each gear increases by approximately 33.3r / min); Example: When the data is located at position 3 in the first range (corresponding to 0.16 meters), the actual speed = 300 + 2 × 33.3 ≈ 367 r / min; when it is at position 10 (0.3 meters), the speed = 300 + 9 × 33.3 ≈ 600 r / min.

[0106] Through this adjustment, low liquid level data (positions 1-3) during non-production periods (such as at night) corresponds to low-speed operation to reduce energy consumption; the early morning production preparation stage (positions 7-10) corresponds to medium-speed operation to activate processing capacity in advance.

[0107] When the data is in the second range, the patrol intervals of the three drainage treatment devices (such as filter pumps) are adjusted in an anti-correlated manner: The preset patrol interval range is 5-15 minutes (the single running time of each device); Adjustment formula: actual interval = 15-(sort position-1) × 1.11 (each gear decreases by approximately 1.11 minutes); For example, if the data is located at position 2 in the second range (corresponding to 0.33 meters), the actual interval is 15-1×1.11≈13.9 minutes. If the data is located at position 9 (0.57 meters), the interval is 15-8×1.11≈6.1 minutes.

[0108] High-ranking positions (such as positions 8-10) correspond to short intervals, and equipment patrols are more frequent to avoid dye deposition due to continuous operation of a single device; low-ranking positions (such as positions 1-3) correspond to long intervals to reduce equipment start-up and shutdown losses.

[0109] When the data is in the third range, the total power of the five drainage treatment equipment (such as the lift pump) is adjusted in a positive correlation: The preset total power range is 15-30kW, the rated power of a single unit is 6kW, and the total power of 5 units is 30kW; Adjustment formula: Actual total power = 15 + (sort position - 1) × 1.67 (each gear increases by approximately 1.67kW); Example: If the data is located at position 4 in the third range (corresponding to 0.72 meters), the total power = 15 + 3 × 1.67 ≈ 20 kW, which can be allocated as 3 units running at full power + 2 units running at half power. At position 10 (1.0 meters), the total power = 30 kW, and all 5 units are running at full power.

[0110] This regulation ensures that high liquid level data during production peaks (such as noon) corresponds to full-load processing to avoid wastewater backlog; high-range low-ranking positions (such as positions 1-3) correspond to medium power to balance energy consumption and processing needs.

[0111] Safety thresholds are set: the speed in the first range must not fall below 300 rpm to prevent equipment stalls and dye deposits; the total power in the third range must not exceed 30 kW to prevent circuit overload. The system analyzes the effectiveness of adjustments daily. If a certain gear is not handled promptly, such as wastewater overflow at position 10 in the second range, the interval parameters for that gear will be automatically adjusted, for example, from 5 minutes to 4 minutes, achieving dynamic adaptation.

[0112] An embodiment of the present application further discloses a printing and dyeing drainage status analysis system based on sensor data, comprising a processor, wherein the processor executes the steps of any one of the above-mentioned printing and dyeing drainage status analysis methods based on sensor data.

[0113] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for analyzing the drainage status of printing and dyeing based on sensor data, characterized in that: The steps include: Acquiring printing and dyeing drainage data based on preset printing and dyeing drainage sensors, wherein the printing and dyeing drainage sensors are classified into a plurality of groups, wherein each group of printing and dyeing drainage sensors is located at different locations with equal spacing on the same printing and dyeing drainage pipe; periodically recording each group of printing and dyeing drainage data, and processing each group of printing and dyeing drainage data having drainage status information into a printing and dyeing drainage data curve; Using the printing and dyeing drainage data curve corresponding to the midstream or downstream position of the printing and dyeing drainage pipe as a basic template, matching similar intervals with other printing and dyeing drainage data curves, extracting the time intervals of the matched similar intervals, calculating the time difference between the time intervals of the printing and dyeing drainage data curves adjacent to the corresponding positions; and calculating the time discrete values ​​of multiple time differences; If the time discrete value is greater than a preset discrete reference value, a sensor abnormality prompt is issued; Otherwise, calculating the content average of the printing and dyeing drainage data at the upstream position of the printing and dyeing drainage pipe, and matching an execution instruction from a preset execution instruction library according to the content average; In response to the execution instruction, a plurality of drainage treatment devices are controlled to operate: if the printing and dyeing drainage data is within a preset first range, a single drainage treatment device is controlled to operate continuously; A pop-up window prompt is displayed; if the printing and dyeing drainage data is within the preset second range, multiple drainage treatment devices are controlled to perform patrol work, wherein the number of drainage treatment devices working simultaneously is greater than one; and an early warning prompt is displayed; if the printing and dyeing drainage data is within the preset third range, all drainage treatment devices are controlled to work simultaneously and an alarm prompt is displayed; The values ​​in the first range are smaller than the values ​​in the second range, and the values ​​in the second range are smaller than the values ​​in the third range.

2. The method for analyzing the printing and dyeing drainage status based on sensor data according to claim 1, characterized in that: The step of obtaining printing and dyeing drainage data based on the preset printing and dyeing drainage sensor further includes the following sub-steps: The printing and dyeing drainage sensor is a plurality of liquid level sensors, and the liquid level sensor generates liquid level data; Matching a corresponding fluctuation gain from a preset pipeline fluctuation database according to the position data of the liquid level sensor; Matching a liquid level reference range according to the fluctuation gain; Calculating a plurality of the liquid level height values ​​to obtain a comprehensive liquid level value; If the comprehensive value of the liquid level is outside the preset liquid level reference range, a sensor abnormality prompt is issued; otherwise, the comprehensive value of the liquid level is used as the printing and dyeing drainage data.

3. The method for analyzing the printing and dyeing drainage status based on sensor data according to claim 1, characterized in that: The step of obtaining printing and dyeing drainage data based on the preset printing and dyeing drainage sensor further includes the following sub-steps: The printing and dyeing drainage sensor is a plurality of flow rate sensors, and the flow rate sensor generates flow rate data; Matching a corresponding bayonet gain from a preset pipeline bayonet database according to the position data of the flow velocity sensor; Calculating an actual flow rate value according to the flow rate data and the bayonet gain; Calculating a plurality of the actual flow rate values ​​to obtain a flow rate comprehensive value; If the comprehensive flow rate value is outside the preset flow rate reference range, a sensor abnormality prompt is issued; otherwise, the comprehensive flow rate value is used as the printing and dyeing drainage data.

4. The method for analyzing the printing and dyeing drainage status based on sensor data according to claim 2 or 3, characterized in that: It also includes the following sub-steps: Calculating the time when the printing and dyeing drainage data is within the first range as a first time, calculating the time when the printing and dyeing drainage data is within the second range as a second time, and calculating the time when the printing and dyeing drainage data is within the third range as a third time; If the first time is the longest, the ratio of the first time to the second time is calculated as the first time ratio, and the value in the pipeline fluctuation database or the pipeline bayonet database is adjusted in a positive correlation according to the multiplication result of the first time ratio and a preset slow gain value; Otherwise, the ratio of the second time to the third time is calculated as a second time ratio, and the value in the pipeline fluctuation database or the pipeline bayonet database is adjusted inversely according to the multiplication result of the second time ratio and the preset fast gain value.

5. The method for analyzing the printing and dyeing drainage status based on sensor data according to claim 3, characterized in that: It also includes the following sub-steps: Deliver multiple detection particles upstream; A particle sensor is provided downstream, and the particle sensor is capable of measuring and outputting velocity data of the detected particles; sorting the plurality of speed data from largest to smallest; Extracting the velocity data that ranks at the front and reaches a preset percentage, and calculating the average value of the extracted velocity data as a flow velocity comparison value; obtaining the integrated flow velocity value associated with the position of the particle sensor; Calculating a flow velocity difference between the flow velocity comprehensive value and the flow velocity comparison value; If the absolute value of the flow velocity difference is greater than a preset reference absolute value, a pipeline underflow warning prompt is issued.

6. The method for analyzing the printing and dyeing drainage status based on sensor data according to claim 5, characterized in that: The method further comprises the steps of: The detection particles are injection-molded spheres suspended in the printing and dyeing wastewater, and a spherical metal core is provided in the injection-molded spheres. The particle sensor is a metal detector, and the metal detector is used to detect the moving speed of the metal core to obtain the flow rate data. Alternatively, the detection particles are injection-molded spheres suspended in printing and dyeing wastewater, a spherical magnetic core is provided in the injection-molded spheres, the particle sensor is a magnetic field detector, at least two particle sensors are provided, and are separated by a preset detection distance, and the flow rate data is calculated based on the time difference between the two particle sensors detecting the same detection particle and the detection distance.

7. The method for analyzing the printing and dyeing drainage status based on sensor data according to claim 1, characterized in that: The method further comprises the steps of: Acquiring speed data, voltage data, and current data of the drainage treatment equipment; Calculating a speed falling edge of the rotational speed data, a voltage falling edge of the voltage data, and a current rising edge of the current data; Matching the first time of the speed falling edge, the second time of the voltage falling edge, and the third time of the current rising edge; If the time differences between any two of the first time, the second time, and the third time are all within a preset minimum unit time range, a drainage treatment equipment failure warning prompt is issued.

8. The method for analyzing the printing and dyeing drainage status based on sensor data according to claim 7, characterized in that: It also includes the following sub-steps: Calculating a speed component according to the value of the speed falling edge and a preset speed reference value; Calculating a voltage component according to the value of the voltage falling edge and a preset voltage reference value; Calculating a current component according to the value of the current rising edge and a preset current reference value; Calculating the equipment abnormality according to the speed component, voltage component and current component; If the abnormal amount of the equipment is greater than the preset abnormal reference amount, a drainage treatment equipment failure alarm prompt will be issued.

9. The method for analyzing the printing and dyeing drainage status based on sensor data according to claim 1, characterized in that: It also includes the following sub-steps: According to the data sorting position of the printing and dyeing drainage data in the first range from small to large, the rotation speed of the single drainage treatment device is positively correlated; the smaller the data sorting position of the printing and dyeing drainage data in the first range, the lower the rotation speed of the drainage treatment device; the larger the data sorting position of the printing and dyeing drainage data in the first range, the higher the rotation speed of the drainage treatment device; According to the data sorting position of the printing and dyeing drainage data in the second range from small to large, the time interval of the drainage treatment device performing the patrol work is adjusted in an anti-correlation manner; the smaller the data sorting position of the printing and dyeing drainage data in the second range, the longer the time interval of the drainage treatment device performing the patrol work; the larger the data sorting position of the printing and dyeing drainage data in the second range, the shorter the time interval of the drainage treatment device performing the patrol work; According to the data sorting position of the printing and dyeing drainage data from small to large within the third range, the total power of all the drainage treatment equipment is adjusted in a positive correlation; the smaller the data sorting position of the printing and dyeing drainage data within the third range, the smaller the total power of all the drainage treatment equipment; the larger the data sorting position of the printing and dyeing drainage data within the third range, the greater the total power of all the drainage treatment equipment.

10. A printing and dyeing drainage status analysis system based on sensor data, characterized in that: The method comprises a processor, wherein the processor executes the steps of the method for analyzing the printing and dyeing drainage status based on sensor data as described in any one of claims 1 to 9.

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